├── sample └── utils │ ├── __init__.py │ ├── stacked_auto_encoder.py │ ├── data_set_settings.ini │ ├── lncPro_interface.py │ ├── basic_modules.py │ ├── callbacks.py │ └── sequence_encoder.py ├── .gitattributes ├── requirements.txt ├── result ├── RPI369 │ ├── RPI369-18-07-15-11-30-05 │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 5 of RPITER stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 5 of Conjoint-CNN stage.pdf │ │ ├── fold 5 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 5 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 5 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt │ └── RPI369-20-04-29-08-22-51 │ │ ├── fold 0 of RPITER stage.pdf │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 0 of Conjoint-CNN stage.pdf │ │ ├── fold 0 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 0 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 0 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt ├── RPI488 │ ├── RPI488-18-07-21-16-39-11 │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 5 of RPITER stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 5 of Conjoint-CNN stage.pdf │ │ ├── fold 5 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 5 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 5 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt │ └── RPI488-20-04-29-08-23-32 │ │ ├── fold 0 of RPITER stage.pdf │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 0 of Conjoint-CNN stage.pdf │ │ ├── fold 0 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 0 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 0 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt ├── NPInter │ ├── NPInter-18-07-21-11-36-33 │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 5 of RPITER stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 5 of Conjoint-CNN stage.pdf │ │ ├── fold 5 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 5 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 5 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt │ └── NPInter-20-04-29-09-29-48 │ │ ├── fold 0 of RPITER stage.pdf │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 0 of Conjoint-CNN stage.pdf │ │ ├── fold 0 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 0 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 0 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt ├── RPI1807 │ ├── RPI1807-18-07-20-22-25-08 │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 5 of RPITER stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 5 of Conjoint-CNN stage.pdf │ │ ├── fold 5 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 5 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 5 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt │ └── RPI1807-20-04-29-08-22-51 │ │ ├── fold 0 of RPITER stage.pdf │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 0 of Conjoint-CNN stage.pdf │ │ ├── fold 0 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 0 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 0 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt ├── RPI2241 │ ├── RPI2241-18-07-14-23-53-09 │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 5 of RPITER stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 5 of Conjoint-CNN stage.pdf │ │ ├── fold 5 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 5 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 5 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt │ └── RPI2241-20-04-29-08-22-51 │ │ ├── fold 0 of RPITER stage.pdf │ │ ├── fold 1 of RPITER stage.pdf │ │ ├── fold 2 of RPITER stage.pdf │ │ ├── fold 3 of RPITER stage.pdf │ │ ├── fold 4 of RPITER stage.pdf │ │ ├── fold 0 of Conjoint-CNN stage.pdf │ │ ├── fold 0 of Conjoint-SAE stage.pdf │ │ ├── fold 1 of Conjoint-CNN stage.pdf │ │ ├── fold 1 of Conjoint-SAE stage.pdf │ │ ├── fold 2 of Conjoint-CNN stage.pdf │ │ ├── fold 2 of Conjoint-SAE stage.pdf │ │ ├── fold 3 of Conjoint-CNN stage.pdf │ │ ├── fold 3 of Conjoint-SAE stage.pdf │ │ ├── fold 4 of Conjoint-CNN stage.pdf │ │ ├── fold 4 of Conjoint-SAE stage.pdf │ │ ├── fold 0 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 0 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 1 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 1 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 2 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 2 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 3 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 3 of Conjoint-Struct-SAE stage.pdf │ │ ├── fold 4 of Conjoint-Struct-CNN stage.pdf │ │ ├── fold 4 of Conjoint-Struct-SAE stage.pdf │ │ └── result.txt └── readme.md ├── LICENSE ├── data ├── readme.md ├── RPI369_pos_pairs.txt ├── RPI488_pairs.txt └── RPI369_pairs.txt ├── .gitignore └── README.md /sample/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform 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It has been noted that the underlying updates in DL 3 | python packages could influence the program performance mainly on RPI488 (for the very limited data size). 4 | Thus, users are suggested to use below python running environment to test the program. 5 | Anaconda is recommended to be employed in creating and managing the environment. 6 | 7 | Package | Version 8 | -------|------- 9 | python | 3.6.10 10 | tensorflow(-gpu) | 1.10.0 11 | keras | 2.2.2 12 | numpy | 1.18.1 13 | scikit-learn | 0.22.1 14 | matplotlib | 3.1.3 15 | h5py | 2.10.0 -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Pengeace 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. -------------------------------------------------------------------------------- /data/readme.md: -------------------------------------------------------------------------------- 1 | ### (Non)-Interaction pairs 2 | 3 | Let *XXX* be a dataset name, i. e. *RPI369*, *RPI488*, *RPI1807*, *RPI2241* or *NPInter*. 4 | In *XXX_pairs.txt*, each line means an (non)-interactive protein RNA pair. 5 | 6 | ``` 7 | Protein RNA Label 8 | ``` 9 | 10 | Here, `Protein` and `RNA` are corresponding sequence names. `Label` is 1 or 0 representing interaction or non-interaction. 11 | 12 | ### Primary sequence data 13 | 14 | Directory *sequence* is about the primary sequence data of protein and RNA. 15 | In *XXX_protein_seq.fa* or *XXX_rna_seq.fa*, each `Protein` or `RNA` name is followed by its sequence data in the next line. 16 | 17 | For example, in *RPI1807_rna_seq.fa*, the initial two lines show the primary sequence data of RNA `3OVB-C`. 18 | 19 | ``` 20 | >3OVB-C 21 | GGAAGUAGAUGGUUCAAGUCCAUUUACUUCCACCA 22 | ``` 23 | 24 | ### Sequence structure data 25 | 26 | Directory *structure* is about the predicted sequence structure data of protein and RNA. 27 | In *XXX_protein_struct.fa* or *XXX_rna_struct.fa*, each `Protein` or `RNA` name is followed by its predicted sequence structure (3 states of Helix, Sheet and Coil for protein; two states of dot-bracket format for rna) in the next line. 28 | 29 | For example, in *RPI1807_rna_struct.fa*, the initial two lines show the predicted sequence structure of RNA `3OVB-C`. 30 | 31 | ``` 32 | >3OVB-C 33 | ((((((((((((.......)))))))))))).... 34 | ``` 35 | -------------------------------------------------------------------------------- /sample/utils/stacked_auto_encoder.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from keras.layers import Dense, Dropout 3 | from keras.models import Sequential 4 | 5 | 6 | def train_auto_encoder(X_train, X_test, layers, batch_size=100, nb_epoch=100, activation='sigmoid', optimizer='adam'): 7 | trained_encoders = [] 8 | trained_decoders = [] 9 | X_train_tmp = np.copy(X_train) 10 | X_test_tmp = np.copy(X_test) 11 | for n_in, n_out in zip(layers[:-1], layers[1:]): 12 | print('Pre-training the layer: Input {} -> Output {}'.format(n_in, n_out)) 13 | ae = Sequential( 14 | [Dense(n_out, input_dim=X_train_tmp.shape[1], activation=activation, ), 15 | Dense(n_in, activation=activation), 16 | Dropout(0.2)] 17 | ) 18 | ae.compile(loss='mean_squared_error', optimizer=optimizer) 19 | ae.fit(X_train_tmp, X_train_tmp, batch_size=batch_size, epochs=nb_epoch, verbose=0, shuffle=True) 20 | # store trained encoder 21 | trained_encoders.append(ae.layers[0]) 22 | trained_decoders.append(ae.layers[1]) 23 | # update training data 24 | encoder = Sequential([ae.layers[0]]) 25 | # encoder.evaluate(X_train_tmp, X_train_tmp, batch_size=batch_size) 26 | X_train_tmp = encoder.predict(X_train_tmp) 27 | X_test_tmp = encoder.predict(X_test_tmp) 28 | 29 | return trained_encoders, trained_decoders, X_train_tmp, X_test_tmp 30 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | *.egg-info/ 24 | .installed.cfg 25 | *.egg 26 | MANIFEST 27 | 28 | # PyInstaller 29 | # Usually these files are written by a python script from a template 30 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 31 | *.manifest 32 | *.spec 33 | 34 | # Installer logs 35 | pip-log.txt 36 | pip-delete-this-directory.txt 37 | 38 | # Unit test / coverage reports 39 | htmlcov/ 40 | .tox/ 41 | .coverage 42 | .coverage.* 43 | .cache 44 | nosetests.xml 45 | coverage.xml 46 | *.cover 47 | .hypothesis/ 48 | .pytest_cache/ 49 | 50 | # Translations 51 | *.mo 52 | *.pot 53 | 54 | # Django stuff: 55 | *.log 56 | local_settings.py 57 | db.sqlite3 58 | 59 | # Flask stuff: 60 | instance/ 61 | .webassets-cache 62 | 63 | # Scrapy stuff: 64 | .scrapy 65 | 66 | # Sphinx documentation 67 | docs/_build/ 68 | 69 | # PyBuilder 70 | target/ 71 | 72 | # Jupyter Notebook 73 | .ipynb_checkpoints 74 | 75 | # pyenv 76 | .python-version 77 | 78 | # celery beat schedule file 79 | celerybeat-schedule 80 | 81 | # SageMath parsed files 82 | *.sage.py 83 | 84 | # Environments 85 | .env 86 | .venv 87 | env/ 88 | venv/ 89 | ENV/ 90 | env.bak/ 91 | venv.bak/ 92 | 93 | # Spyder project settings 94 | .spyderproject 95 | .spyproject 96 | 97 | # Rope project settings 98 | .ropeproject 99 | 100 | # mkdocs documentation 101 | /site 102 | 103 | # mypy 104 | .mypy_cache/ 105 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # RPITER 2 | A hierarchical deep learning model for predicting ncRNA-protein interaction. 3 | 4 | The _sample_, _data_ and _result_ directories contain model codes, tested data sets and generated results, respectively. 5 | The depended python packages are listed in _requirements.txt_. The package versions should be followed by users in their environments to achieve the supposed performance. 6 | 7 | ## How to run 8 | 9 | The program is in Python 3.6 using [Keras](https://keras.io/) and [Tensorflow](https://www.tensorflow.org/) backends. Use the below bash command to run RPITER. 10 | 11 | ```bash 12 | python rpiter.py -d dataset 13 | ``` 14 | 15 | The parameter of _dataset_ could be RPI369, RPI488, RPI1807, RPI2241 or NPInter. Then, RPITER will perform 5-fold cross validation on the specific dataset. 16 | 17 | 18 | ## Five RPI datasets 19 | 20 | The widely used RPI benchmark datasets are organized in the _data_ directory. 21 | 22 | Dataset | #Positive pairs | #Negative pairs | RNAs | Proteins | Reference 23 | -------|----|------|----|-------|---------- 24 | RPI369 | 369 | 0 | 332 | 338 | [1] 25 | RPI488 | 243 | 245 | 25 | 247 | [2] 26 | RPI1807 | 1807 | 1436 | 1078 | 3131 | [3] 27 | RPI2241 | 2241 | 0 | 841 | 2042 | [1] 28 | NPInter | 10412 | 0 | 4636 | 449 | [4] 29 | 30 | ## Help 31 | 32 | For any questions, feel free to contact me by chengpengeace@gmail.com or start an issue instead. 33 | 34 | 35 | 36 | [1] Muppirala, U.K.; Honavar, V.G.; Dobbs, D. Predicting RNA-Protein Interactions Using Only Sequence Information. Bmc Bioinformatics 2011, 12. doi:Artn 489 10.1186/1471-2105-12-489. 37 | 38 | [2] Pan, X.Y.; Fan, Y.X.; Yan, J.C.; Shen, H.B. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. Bmc Genomics 2016, 17. doi:ARTN 582 10.1186/s12864-016-2931-8. 39 | 40 | [3] Suresh, V.; Liu, L.; Adjeroh, D.; Zhou, X.B. RPI-Pred: predicting ncRNA-protein interaction using sequence and structural information. Nucleic Acids Research 2015, 43, 1370–1379. doi:10.1093/nar/gkv020. 41 | 42 | [4] Yuan, J.;Wu,W.; Xie, C.Y.; Zhao, G.G.; Zhao, Y.; Chen, R.S. NPInter v2.0: an updated database of ncRNA interactions. Nucleic Acids Research 2014, 42, D104–D108. doi:10.1093/nar/gkt1057. 43 | -------------------------------------------------------------------------------- /sample/utils/data_set_settings.ini: -------------------------------------------------------------------------------- 1 | [RPI369] 2 | WINDOW_P_UPLIMIT = 3 3 | WINDOW_P_STRUCT_UPLIMIT = 3 4 | WINDOW_R_UPLIMIT = 4 5 | WINDOW_R_STRUCT_UPLIMIT = 4 6 | VECTOR_REPETITION_CNN = 1 7 | RANDOM_SEED = 1 8 | K_FOLD = 5 9 | BATCH_SIZE = 80 10 | FIRST_TRAIN_EPOCHS = [20, 20, 20, 20, 10] 11 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 15] 12 | PATIENCES = [10, 10, 10, 10, 10] 13 | FIRST_OPTIMIZER = adam 14 | SECOND_OPTIMIZER = sgd 15 | SGD_LEARNING_RATE = 0.01 16 | ADAM_LEARNING_RATE = 0.001 17 | FREEZE_SUB_MODELS = False 18 | CODING_FREQUENCY = True 19 | MONITOR = acc 20 | MIN_DELTA = 0.0 21 | 22 | 23 | [RPI488] 24 | WINDOW_P_UPLIMIT = 3 25 | WINDOW_P_STRUCT_UPLIMIT = 3 26 | WINDOW_R_UPLIMIT = 4 27 | WINDOW_R_STRUCT_UPLIMIT = 4 28 | VECTOR_REPETITION_CNN = 1 29 | RANDOM_SEED = 1 30 | K_FOLD = 5 31 | BATCH_SIZE = 80 32 | FIRST_TRAIN_EPOCHS = [3, 3, 6, 6, 3] 33 | SECOND_TRAIN_EPOCHS = [3, 3, 6, 6, 3] 34 | PATIENCES = [10, 10, 10, 10, 10] 35 | FIRST_OPTIMIZER = adam 36 | SECOND_OPTIMIZER = sgd 37 | SGD_LEARNING_RATE = 0.001 38 | ADAM_LEARNING_RATE = 0.001 39 | FREEZE_SUB_MODELS = True 40 | CODING_FREQUENCY = True 41 | MONITOR = acc 42 | MIN_DELTA = 0.0 43 | 44 | 45 | [RPI1807] 46 | WINDOW_P_UPLIMIT = 3 47 | WINDOW_P_STRUCT_UPLIMIT = 3 48 | WINDOW_R_UPLIMIT = 4 49 | WINDOW_R_STRUCT_UPLIMIT = 4 50 | VECTOR_REPETITION_CNN = 1 51 | RANDOM_SEED = 1 52 | K_FOLD = 5 53 | BATCH_SIZE = 150 54 | FIRST_TRAIN_EPOCHS = [25, 25, 25, 25, 15] 55 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10] 56 | PATIENCES = [10, 10, 10, 10, 10] 57 | FIRST_OPTIMIZER = adam 58 | SECOND_OPTIMIZER = sgd 59 | SGD_LEARNING_RATE = 0.005 60 | ADAM_LEARNING_RATE = 0.001 61 | FREEZE_SUB_MODELS = True 62 | CODING_FREQUENCY = True 63 | MONITOR = acc 64 | MIN_DELTA = 0.0 65 | 66 | 67 | [RPI2241] 68 | WINDOW_P_UPLIMIT = 3 69 | WINDOW_P_STRUCT_UPLIMIT = 3 70 | WINDOW_R_UPLIMIT =4 71 | WINDOW_R_STRUCT_UPLIMIT = 4 72 | VECTOR_REPETITION_CNN = 1 73 | RANDOM_SEED = 1 74 | K_FOLD = 5 75 | BATCH_SIZE = 150 76 | FIRST_TRAIN_EPOCHS = [35, 35, 35, 35, 15] 77 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10] 78 | PATIENCES = [10, 10, 10, 10, 10] 79 | FIRST_OPTIMIZER = adam 80 | SECOND_OPTIMIZER = sgd 81 | SGD_LEARNING_RATE = 0.01 82 | ADAM_LEARNING_RATE = 0.001 83 | FREEZE_SUB_MODELS = False 84 | CODING_FREQUENCY = True 85 | MONITOR = acc 86 | MIN_DELTA = 0.0 87 | 88 | 89 | [NPInter] 90 | WINDOW_P_UPLIMIT = 3 91 | WINDOW_P_STRUCT_UPLIMIT = 3 92 | WINDOW_R_UPLIMIT = 4 93 | WINDOW_R_STRUCT_UPLIMIT = 4 94 | VECTOR_REPETITION_CNN = 1 95 | RANDOM_SEED = 1 96 | K_FOLD = 5 97 | BATCH_SIZE = 150 98 | FIRST_TRAIN_EPOCHS = [35, 35, 35, 35, 15] 99 | SECOND_TRAIN_EPOCHS = [15, 15, 15, 15, 15] 100 | PATIENCES = [10, 10, 10, 10, 10] 101 | FIRST_OPTIMIZER = adam 102 | SECOND_OPTIMIZER = sgd 103 | SGD_LEARNING_RATE = 0.005 104 | ADAM_LEARNING_RATE = 0.001 105 | FREEZE_SUB_MODELS = False 106 | CODING_FREQUENCY = True 107 | MONITOR = acc 108 | MIN_DELTA = 0.0 -------------------------------------------------------------------------------- /sample/utils/lncPro_interface.py: -------------------------------------------------------------------------------- 1 | import os 2 | import sys 3 | import math 4 | from sklearn.metrics import confusion_matrix 5 | from sklearn.metrics import roc_curve, auc 6 | 7 | dataset = sys.argv[1] 8 | data_path = './data/' 9 | 10 | def read_data_pair(path): 11 | print("Reading RPI pairs...") 12 | pos_pairs = [] 13 | neg_pairs = [] 14 | with open(path, 'r') as f: 15 | for line in f: 16 | line = line.strip() 17 | p, r, label = line.split('\t') 18 | if label == '1': 19 | pos_pairs.append((p, r)) 20 | elif label == '0': 21 | neg_pairs.append((p, r)) 22 | return pos_pairs, neg_pairs 23 | 24 | def read_data_seq(path): 25 | seq_dict = {} 26 | with open(path, 'r') as f: 27 | name = '' 28 | for line in f: 29 | line = line.strip() 30 | if line[0] == '>': 31 | name = line[1:] 32 | seq_dict[name] = '' 33 | else: 34 | if line.startswith('XXX'): 35 | seq_dict.pop(name) 36 | else: 37 | seq_dict[name] = line 38 | return seq_dict 39 | 40 | def create_seq_file(path, seq_name, seq): 41 | with open(path, 'w') as f: 42 | f.write('>{}\n{}\n'.format(seq_name, seq)) 43 | 44 | def exec_cmd(cmd): 45 | r = os.popen(cmd) 46 | text = r.read() 47 | r.close() 48 | return text 49 | 50 | # calculate the 6 metrics of Acc, Sn, Sp, Precision, MCC and AUC 51 | def calc_metrics(y_label, y_proba): 52 | con_matrix = confusion_matrix(y_label, [1 if x >= 0.5 else 0 for x in y_proba]) 53 | TN = con_matrix[0][0] 54 | FP = con_matrix[0][1] 55 | FN = con_matrix[1][0] 56 | TP = con_matrix[1][1] 57 | P = TP + FN 58 | N = TN + FP 59 | Sn = TP / P if P > 0 else 0 60 | Sp = TN / N if N > 0 else 0 61 | Acc = (TP + TN) / (P + N) if (P + N) > 0 else 0 62 | Pre = (TP) / (TP + FP) if (TP+FP) > 0 else 0 63 | MCC = 0 64 | tmp = math.sqrt((TP + FP) * (TP + FN)) * math.sqrt((TN + FP) * (TN + FN)) 65 | if tmp != 0: 66 | MCC = (TP * TN - FP * FN) / tmp 67 | fpr, tpr, thresholds = roc_curve(y_label, y_proba) 68 | AUC = auc(fpr, tpr) 69 | return Acc, Sn, Sp, Pre, MCC, AUC 70 | 71 | def predict(pair, pro_seq, rna_seq): 72 | create_seq_file('./pro.txt', pair[0], pro_seq) 73 | create_seq_file('./rna.txt', pair[1], rna_seq) 74 | result = exec_cmd('./RNAScore pro.txt rna.txt') 75 | return float(result) 76 | 77 | print("Procesing dataset %s" % (dataset)) 78 | 79 | # load data 80 | print("Loading data...") 81 | pos_pairs, neg_pairs = read_data_pair(data_path + dataset +'_pairs.txt') 82 | pro_seq_dict = read_data_seq(data_path + 'sequence/' + dataset +'_protein_seq.fa') 83 | rna_seq_dict = read_data_seq(data_path + 'sequence/' + dataset +'_rna_seq.fa') 84 | 85 | print('Predicting...') 86 | predictions = [] 87 | for pr in pos_pairs: 88 | result = predict(pr, pro_seq_dict[pr[0]], rna_seq_dict[pr[1]]) 89 | print('{}\t{}\t{}'.format(pr, 1, result)) 90 | predictions.append(result) 91 | for pr in neg_pairs: 92 | result = predict(pr, pro_seq_dict[pr[0]], rna_seq_dict[pr[1]]) 93 | print('{}\t{}\t{}'.format(pr, 0, result)) 94 | predictions.append(result) 95 | 96 | metrics = calc_metrics([1]*len(pos_pairs)+[0]*len(neg_pairs), predictions) 97 | print(metrics) 98 | 99 | with open('./%s_result.txt' % dataset, 'w') as r: 100 | r.write("# lncPro performances on %s (Acc, Sn, Sp, Pre, MCC, AUC):\n" % dataset) 101 | r.write(str(metrics)+'\n') 102 | r.write('\n\nDetailed prediction results (protein, RNA, label, prediction):\n') 103 | pos_num = len(pos_pairs) 104 | neg_num = len(neg_pairs) 105 | for i in range(pos_num): 106 | r.write('{}\t{}\t{}\t{}'.format(pr[0],pr[1],1,predictions[i])) 107 | for i in range(neg_num): 108 | r.write('{}\t{}\t{}\t{}'.format(pr[0],pr[1],1,predictions[i+pos_num])) -------------------------------------------------------------------------------- /result/RPI2241/RPI2241-18-07-14-23-53-09/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI2241 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [33, 33, 33, 33, 15], 13 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | CODING_FREQUENCY = True, 18 | MONITOR = acc, 19 | MIN_DELTA = 0.0, 20 | 21 | Pos samples: 2241, Neg samples: 2241. 22 | 23 | 24 | K-fold cross validation processes: 25 | 26 | # ========== Fold 1 ========== 27 | Conjoint-Struct-SAE : [0.8418708240534521, 0.89086859688196, 0.7928730512249443, 0.8113590263691683, 0.6870485133744544, 0.9329120391267901] 28 | Conjoint-CNN : [0.878619153674833, 0.8975501113585747, 0.8596881959910914, 0.8648068669527897, 0.7577816514765823, 0.9486064057221939] 29 | Conjoint-Struct-CNN : [0.8819599109131403, 0.9020044543429844, 0.8619153674832962, 0.867237687366167, 0.7645344238344146, 0.953680785313565] 30 | RPITER : [0.89086859688196, 0.8953229398663697, 0.8864142538975501, 0.8874172185430463, 0.7817682167738687, 0.9560741266164355] 31 | Conjoint-SAE : [0.8541202672605791, 0.910913140311804, 0.7973273942093542, 0.818, 0.7128539850629304, 0.9353872252617794] 32 | 33 | # ========== Fold 2 ========== 34 | Conjoint-Struct-SAE : [0.8638392857142857, 0.90625, 0.8214285714285714, 0.8353909465020576, 0.7303104871273246, 0.9420290577168366] 35 | Conjoint-CNN : [0.8816964285714286, 0.9196428571428571, 0.84375, 0.8547717842323651, 0.7656008666352222, 0.9566725127551019] 36 | Conjoint-Struct-CNN : [0.8683035714285714, 0.90625, 0.8303571428571429, 0.8423236514522822, 0.738737678332232, 0.9503124003507653] 37 | RPITER : [0.8794642857142857, 0.921875, 0.8370535714285714, 0.8497942386831275, 0.7616735141818722, 0.9521584024234693] 38 | Conjoint-SAE : [0.8571428571428571, 0.8995535714285714, 0.8147321428571429, 0.8292181069958847, 0.7168691898182327, 0.9428162866709183] 39 | 40 | # ========== Fold 3 ========== 41 | Conjoint-Struct-SAE : [0.8616071428571429, 0.9241071428571429, 0.7991071428571429, 0.8214285714285714, 0.7289314836606525, 0.9405343191964286] 42 | Conjoint-CNN : [0.8828125, 0.9174107142857143, 0.8482142857142857, 0.8580375782881002, 0.7674645707780969, 0.9535460180165816] 43 | Conjoint-Struct-CNN : [0.8783482142857143, 0.9241071428571429, 0.8325892857142857, 0.8466257668711656, 0.7598853365245236, 0.9541588608099489] 44 | RPITER : [0.8962053571428571, 0.9330357142857143, 0.859375, 0.8690228690228691, 0.7945692733396572, 0.9571109693877552] 45 | Conjoint-SAE : [0.8537946428571429, 0.9308035714285714, 0.7767857142857143, 0.8065764023210832, 0.7161341523760646, 0.9362842793367346] 46 | 47 | # ========== Fold 4 ========== 48 | Conjoint-Struct-SAE : [0.8582589285714286, 0.90625, 0.8102678571428571, 0.8268839103869654, 0.7198413241963948, 0.9341742067920917] 49 | Conjoint-CNN : [0.8883928571428571, 0.8973214285714286, 0.8794642857142857, 0.881578947368421, 0.7769095936763887, 0.9517647879464286] 50 | Conjoint-Struct-CNN : [0.8850446428571429, 0.9107142857142857, 0.859375, 0.8662420382165605, 0.7711061670543424, 0.9531374561543366] 51 | RPITER : [0.8861607142857143, 0.9241071428571429, 0.8482142857142857, 0.8589211618257261, 0.774555262736219, 0.9565653898278061] 52 | Conjoint-SAE : [0.8627232142857143, 0.9263392857142857, 0.7991071428571429, 0.8217821782178217, 0.731390463312876, 0.9427589883609694] 53 | 54 | # ========== Fold 5 ========== 55 | Conjoint-Struct-SAE : [0.8604910714285714, 0.9196428571428571, 0.8013392857142857, 0.8223552894211577, 0.7260810656105464, 0.9402901785714286] 56 | Conjoint-CNN : [0.8928571428571429, 0.90625, 0.8794642857142857, 0.8826086956521739, 0.78599630245636, 0.9608727279974489] 57 | Conjoint-Struct-CNN : [0.8794642857142857, 0.8950892857142857, 0.8638392857142857, 0.8679653679653679, 0.7592994136551628, 0.9512441206951531] 58 | RPITER : [0.8973214285714286, 0.9084821428571429, 0.8861607142857143, 0.888646288209607, 0.7948408950310686, 0.9621631855867347] 59 | Conjoint-SAE : [0.8694196428571429, 0.9084821428571429, 0.8303571428571429, 0.8426501035196687, 0.7411044185218386, 0.9383071588010203] 60 | 61 | Mean metrics in 5 fold: 62 | Conjoint-Struct-SAE : [0.857213450524976, 0.909423719376392, 0.8050031816735602, 0.8234835488215839, 0.7184425747938745, 0.9379879602807151] 63 | Conjoint-CNN : [0.8848756164492524, 0.9076350222717149, 0.8621162106267898, 0.86836077449877, 0.77075059700453, 0.954292490487551] 64 | Conjoint-Struct-CNN : [0.878624125039771, 0.9076330337257398, 0.8496152163538021, 0.8580789023743087, 0.7587126038801351, 0.9525067246647538] 65 | RPITER : [0.8900040765192492, 0.916564587973274, 0.8634435650652243, 0.8707603552568752, 0.7814814324125371, 0.9568144147684402] 66 | Conjoint-SAE : [0.8594401248806873, 0.9152183423480752, 0.8036619074132995, 0.8236453582108917, 0.7236704418183884, 0.9391107876862843] 67 | -------------------------------------------------------------------------------- /result/RPI488/RPI488-18-07-21-16-39-11/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI488 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 80, 12 | FIRST_TRAIN_EPOCHS = [3, 3, 6, 6, 1], 13 | SECOND_TRAIN_EPOCHS = [3, 3, 6, 6, 5], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | CODING_FREQUENCY = True, 18 | MONITOR = acc, 19 | MIN_DELTA = 0.0, 20 | 21 | Pos samples: 243, Neg samples: 245. 22 | 23 | 24 | K-fold cross validation processes: 25 | 26 | # ========== Fold 1 ========== 27 | RPITER : [0.8673469387755102, 0.8571428571428571, 0.8775510204081632, 0.875, 0.7348469228349533, 0.880466472303207] 28 | Conjoint-Struct-SAE : [0.8673469387755102, 0.8367346938775511, 0.8979591836734694, 0.8913043478260869, 0.7360747380985775, 0.8883798417326114] 29 | Conjoint-CNN : [0.8469387755102041, 0.8367346938775511, 0.8571428571428571, 0.8541666666666666, 0.694022093788567, 0.8779675135360266] 30 | Conjoint-Struct-CNN : [0.8673469387755102, 0.8367346938775511, 0.8979591836734694, 0.8913043478260869, 0.7360747380985775, 0.8750520616409829] 31 | Conjoint-SAE : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.876301541024573] 32 | 33 | # ========== Fold 2 ========== 34 | RPITER : [0.9081632653061225, 0.8775510204081632, 0.9387755102040817, 0.9347826086956522, 0.8178608201095305, 0.9379425239483549] 35 | Conjoint-Struct-SAE : [0.9081632653061225, 0.8775510204081632, 0.9387755102040817, 0.9347826086956522, 0.8178608201095305, 0.9187838400666388] 36 | Conjoint-CNN : [0.9081632653061225, 0.8775510204081632, 0.9387755102040817, 0.9347826086956522, 0.8178608201095305, 0.931695127030404] 37 | Conjoint-Struct-CNN : [0.9081632653061225, 0.8775510204081632, 0.9387755102040817, 0.9347826086956522, 0.8178608201095305, 0.9162848812994585] 38 | Conjoint-SAE : [0.9081632653061225, 0.8775510204081632, 0.9387755102040817, 0.9347826086956522, 0.8178608201095305, 0.9441899208663057] 39 | 40 | # ========== Fold 3 ========== 41 | RPITER : [0.8877551020408163, 0.8163265306122449, 0.9591836734693877, 0.9523809523809523, 0.7835467939002064, 0.8663057059558517] 42 | Conjoint-Struct-SAE : [0.8775510204081632, 0.8163265306122449, 0.9387755102040817, 0.9302325581395349, 0.7608274120662152, 0.8763015410245731] 43 | Conjoint-CNN : [0.8877551020408163, 0.8163265306122449, 0.9591836734693877, 0.9523809523809523, 0.7835467939002064, 0.8867138692211579] 44 | Conjoint-Struct-CNN : [0.8673469387755102, 0.7755102040816326, 0.9591836734693877, 0.95, 0.7474093186836597, 0.8837984173261141] 45 | Conjoint-SAE : [0.8877551020408163, 0.8163265306122449, 0.9591836734693877, 0.9523809523809523, 0.7835467939002064, 0.8746355685131196] 46 | 47 | # ========== Fold 4 ========== 48 | RPITER : [0.9278350515463918, 0.875, 0.9795918367346939, 0.9767441860465116, 0.8600947386464731, 0.9672619047619049] 49 | Conjoint-Struct-SAE : [0.9278350515463918, 0.875, 0.9795918367346939, 0.9767441860465116, 0.8600947386464731, 0.959608843537415] 50 | Conjoint-CNN : [0.9072164948453608, 0.8333333333333334, 0.9795918367346939, 0.975609756097561, 0.8227791682724235, 0.9468537414965986] 51 | Conjoint-Struct-CNN : [0.9278350515463918, 0.875, 0.9795918367346939, 0.9767441860465116, 0.8600947386464731, 0.9617346938775511] 52 | Conjoint-SAE : [0.9278350515463918, 0.875, 0.9795918367346939, 0.9767441860465116, 0.8600947386464731, 0.961734693877551] 53 | 54 | # ========== Fold 5 ========== 55 | RPITER : [0.8762886597938144, 0.7708333333333334, 0.9795918367346939, 0.9736842105263158, 0.7686139224646228, 0.9026360544217686] 56 | Conjoint-Struct-SAE : [0.8762886597938144, 0.7708333333333334, 0.9795918367346939, 0.9736842105263158, 0.7686139224646228, 0.8703231292517006] 57 | Conjoint-CNN : [0.8762886597938144, 0.7708333333333334, 0.9795918367346939, 0.9736842105263158, 0.7686139224646228, 0.8801020408163266] 58 | Conjoint-Struct-CNN : [0.8762886597938144, 0.7708333333333334, 0.9795918367346939, 0.9736842105263158, 0.7686139224646228, 0.901360544217687] 59 | Conjoint-SAE : [0.8762886597938144, 0.7708333333333334, 0.9795918367346939, 0.9736842105263158, 0.7686139224646228, 0.8592687074829932] 60 | 61 | Mean metrics in 5 fold: 62 | RPITER : [0.8934778034925313, 0.8393707482993197, 0.9469387755102041, 0.9425183915298865, 0.7929926395911572, 0.9109225322782175] 63 | Conjoint-Struct-SAE : [0.8914369871660004, 0.8352891156462585, 0.9469387755102041, 0.9413495822468203, 0.7886943262770838, 0.9026794391225879] 64 | Conjoint-CNN : [0.8852724594992637, 0.8269557823129252, 0.9428571428571428, 0.9381248388734296, 0.77736455970707, 0.9046664584201027] 65 | Conjoint-Struct-CNN : [0.8893961708394699, 0.8271258503401361, 0.9510204081632654, 0.9453030706189134, 0.7860107076005727, 0.9076461196723589] 66 | Conjoint-SAE : [0.8914369871660004, 0.8352891156462585, 0.9469387755102041, 0.9419864766362694, 0.7889995448101582, 0.9032260863529086] 67 | -------------------------------------------------------------------------------- /result/RPI369/RPI369-18-07-15-11-30-05/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI369 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 80, 12 | FIRST_TRAIN_EPOCHS = [15, 15, 15, 15, 15], 13 | SECOND_TRAIN_EPOCHS = [15, 15, 15, 15, 15], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | CODING_FREQUENCY = True, 18 | MONITOR = acc, 19 | MIN_DELTA = 0.0, 20 | 21 | Pos samples: 369, Neg samples: 369. 22 | 23 | 24 | K-fold cross validation processes: 25 | 26 | # ========== Fold 1 ========== 27 | Conjoint-Struct-SAE : [0.7364864864864865, 0.8378378378378378, 0.6351351351351351, 0.6966292134831461, 0.482999893740035, 0.8442293644996348] 28 | Conjoint-CNN : [0.7432432432432432, 0.8108108108108109, 0.6756756756756757, 0.7142857142857143, 0.49099025303098287, 0.8124543462381301] 29 | Conjoint-Struct-CNN : [0.6959459459459459, 0.7972972972972973, 0.5945945945945946, 0.6629213483146067, 0.400199911956029, 0.8179327976625274] 30 | RPITER : [0.7027027027027027, 0.8108108108108109, 0.5945945945945946, 0.6666666666666666, 0.4152273992686999, 0.821037253469686] 31 | Conjoint-SAE : [0.7297297297297297, 0.8378378378378378, 0.6216216216216216, 0.6888888888888889, 0.47059105250452654, 0.8299853907962017] 32 | 33 | # ========== Fold 2 ========== 34 | Conjoint-Struct-SAE : [0.7364864864864865, 0.9054054054054054, 0.5675675675675675, 0.6767676767676768, 0.5025189076296059, 0.837472607742878] 35 | Conjoint-CNN : [0.6959459459459459, 0.7567567567567568, 0.6351351351351351, 0.6746987951807228, 0.3948228448008614, 0.8111760409057707] 36 | Conjoint-Struct-CNN : [0.7297297297297297, 0.7702702702702703, 0.6891891891891891, 0.7125, 0.4609772228646444, 0.8283418553688823] 37 | RPITER : [0.75, 0.8378378378378378, 0.6621621621621622, 0.7126436781609196, 0.5078987924210441, 0.8402118334550767] 38 | Conjoint-SAE : [0.7162162162162162, 0.8243243243243243, 0.6081081081081081, 0.6777777777777778, 0.44290922588661324, 0.7912710007304601] 39 | 40 | # ========== Fold 3 ========== 41 | Conjoint-Struct-SAE : [0.7027027027027027, 0.8243243243243243, 0.581081081081081, 0.6630434782608695, 0.41795865943151467, 0.7951059167275383] 42 | Conjoint-CNN : [0.7432432432432432, 0.8243243243243243, 0.6621621621621622, 0.7093023255813954, 0.4930119320982991, 0.8254200146092038] 43 | Conjoint-Struct-CNN : [0.6959459459459459, 0.8378378378378378, 0.5540540540540541, 0.6526315789473685, 0.40869399862053385, 0.7832359386413441] 44 | RPITER : [0.7297297297297297, 0.7972972972972973, 0.6621621621621622, 0.7023809523809523, 0.4637130167514838, 0.820854638422206] 45 | Conjoint-SAE : [0.6351351351351351, 0.7702702702702703, 0.5, 0.6063829787234043, 0.28071730702217296, 0.760591672753835] 46 | 47 | # ========== Fold 4 ========== 48 | Conjoint-Struct-SAE : [0.7094594594594594, 0.9054054054054054, 0.5135135135135135, 0.6504854368932039, 0.4553410708805394, 0.8122717311906501] 49 | Conjoint-CNN : [0.6756756756756757, 0.6621621621621622, 0.6891891891891891, 0.6805555555555556, 0.3514797457833188, 0.7717311906501096] 50 | Conjoint-Struct-CNN : [0.7297297297297297, 0.7972972972972973, 0.6621621621621622, 0.7023809523809523, 0.4637130167514838, 0.8067932797662527] 51 | RPITER : [0.7432432432432432, 0.8243243243243243, 0.6621621621621622, 0.7093023255813954, 0.4930119320982991, 0.8188458728999269] 52 | Conjoint-SAE : [0.7432432432432432, 0.8918918918918919, 0.5945945945945946, 0.6875, 0.5095246653650681, 0.823046018991965] 53 | 54 | # ========== Fold 5 ========== 55 | Conjoint-Struct-SAE : [0.726027397260274, 0.821917808219178, 0.6301369863013698, 0.6896551724137931, 0.4606046390860962, 0.8044661287295929] 56 | Conjoint-CNN : [0.6232876712328768, 0.589041095890411, 0.6575342465753424, 0.6323529411764706, 0.2471557663714904, 0.7305310564833928] 57 | Conjoint-Struct-CNN : [0.6986301369863014, 0.6986301369863014, 0.6986301369863014, 0.6986301369863014, 0.3972602739726027, 0.7744417339088009] 58 | RPITER : [0.7123287671232876, 0.7123287671232876, 0.7123287671232876, 0.7123287671232876, 0.4246575342465753, 0.8022142991180333] 59 | Conjoint-SAE : [0.7054794520547946, 0.8904109589041096, 0.5205479452054794, 0.65, 0.4423258684646914, 0.81497466691687] 60 | 61 | Mean metrics in 5 fold: 62 | Conjoint-Struct-SAE : [0.7222325064790818, 0.8589781562384301, 0.5854868567197334, 0.6753161955637379, 0.4638846341535582, 0.8187091497780589] 63 | Conjoint-CNN : [0.6962791558681969, 0.728619029988893, 0.6639392817475008, 0.6822390663559716, 0.39549210841699045, 0.7902625297773214] 64 | Conjoint-Struct-CNN : [0.7099962976675306, 0.7802665679378008, 0.6397260273972603, 0.6858128033258459, 0.42616888483305876, 0.8021491210695615] 65 | RPITER : [0.7276008885597927, 0.7965198074787117, 0.6586819696408737, 0.7006644779826443, 0.4609017349572205, 0.8206327794729859] 66 | Conjoint-SAE : [0.7059607552758238, 0.8429470566456867, 0.5689744539059608, 0.6621099290780141, 0.4292136238486145, 0.8039737500378663] 67 | -------------------------------------------------------------------------------- /result/RPI1807/RPI1807-18-07-20-22-25-08/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI1807 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [24, 24, 24, 24, 15], 13 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | CODING_FREQUENCY = True, 18 | MONITOR = acc, 19 | MIN_DELTA = 0.0, 20 | 21 | Pos samples: 1807, Neg samples: 1436. 22 | 23 | 24 | K-fold cross validation processes: 25 | 26 | # ========== Fold 1 ========== 27 | RPITER : [0.9692307692307692, 0.9834254143646409, 0.9513888888888888, 0.9621621621621622, 0.9377692766180883, 0.9875354895641496] 28 | Conjoint-Struct-SAE : [0.9753846153846154, 0.9806629834254144, 0.96875, 0.9752747252747253, 0.950105839298585, 0.986753759975445] 29 | Conjoint-CNN : [0.9723076923076923, 0.9834254143646409, 0.9583333333333334, 0.967391304347826, 0.9439342154794114, 0.988451503990178] 30 | Conjoint-Struct-CNN : [0.96, 0.9696132596685083, 0.9479166666666666, 0.9590163934426229, 0.9189059309200442, 0.9871086556169428] 31 | Conjoint-SAE : [0.9661538461538461, 0.9779005524861878, 0.9513888888888888, 0.9619565217391305, 0.9314361049917226, 0.9856171347452424] 32 | 33 | # ========== Fold 2 ========== 34 | RPITER : [0.9738058551617874, 0.988950276243094, 0.9547038327526133, 0.9649595687331537, 0.9471062797203907, 0.9922324677074711] 35 | Conjoint-Struct-SAE : [0.9738058551617874, 0.988950276243094, 0.9547038327526133, 0.9649595687331537, 0.9471062797203907, 0.9931324234315746] 36 | Conjoint-CNN : [0.9738058551617874, 0.988950276243094, 0.9547038327526133, 0.9649595687331537, 0.9471062797203907, 0.9921314031609141] 37 | Conjoint-Struct-CNN : [0.975346687211094, 0.988950276243094, 0.9581881533101045, 0.9675675675675676, 0.9501796571425648, 0.9942008200666064] 38 | Conjoint-SAE : [0.9738058551617874, 0.988950276243094, 0.9547038327526133, 0.9649595687331537, 0.9471062797203907, 0.9912940112037268] 39 | 40 | # ========== Fold 3 ========== 41 | RPITER : [0.9645061728395061, 0.9916897506925207, 0.9303135888501742, 0.9470899470899471, 0.9289648069296119, 0.9883405561400291] 42 | Conjoint-Struct-SAE : [0.9614197530864198, 0.9944598337950139, 0.9198606271777003, 0.9397905759162304, 0.9232524912814558, 0.9879351781250302] 43 | Conjoint-CNN : [0.9614197530864198, 0.9889196675900277, 0.926829268292683, 0.9444444444444444, 0.9226631802008639, 0.9882150819925295] 44 | Conjoint-Struct-CNN : [0.9614197530864198, 0.9889196675900277, 0.926829268292683, 0.9444444444444444, 0.9226631802008639, 0.981198181590047] 45 | Conjoint-SAE : [0.9567901234567902, 0.9889196675900277, 0.9163763066202091, 0.937007874015748, 0.9136229909786241, 0.9869169071587828] 46 | 47 | # ========== Fold 4 ========== 48 | RPITER : [0.9691358024691358, 0.9806094182825484, 0.9547038327526133, 0.9645776566757494, 0.9374874108957858, 0.9863281438512841] 49 | Conjoint-Struct-SAE : [0.9675925925925926, 0.9806094182825484, 0.9512195121951219, 0.9619565217391305, 0.9343892088183875, 0.9846052872875386] 50 | Conjoint-CNN : [0.9629629629629629, 0.9695290858725761, 0.9547038327526133, 0.9641873278236914, 0.9249116284053854, 0.9843157315625392] 51 | Conjoint-Struct-CNN : [0.9645061728395061, 0.9722991689750693, 0.9547038327526133, 0.9642857142857143, 0.9280381462531162, 0.9882778190662793] 52 | Conjoint-SAE : [0.9660493827160493, 0.9750692520775623, 0.9547038327526133, 0.9643835616438357, 0.9311762044431465, 0.9873367629600316] 53 | 54 | # ========== Fold 5 ========== 55 | RPITER : [0.9645061728395061, 0.9833795013850416, 0.9407665505226481, 0.9543010752688172, 0.9283444616508086, 0.9939241557037652] 56 | Conjoint-Struct-SAE : [0.9506172839506173, 0.9529085872576177, 0.9477351916376306, 0.958217270194986, 0.9000185428166099, 0.9916270136187708] 57 | Conjoint-CNN : [0.9645061728395061, 0.9695290858725761, 0.9581881533101045, 0.9668508287292817, 0.9280532038918671, 0.99201308791877] 58 | Conjoint-Struct-CNN : [0.9660493827160493, 0.9722991689750693, 0.9581881533101045, 0.9669421487603306, 0.9311706249822068, 0.9937311185537657] 59 | Conjoint-SAE : [0.9521604938271605, 0.9556786703601108, 0.9477351916376306, 0.9583333333333334, 0.9030957638855145, 0.9921289102087697] 60 | 61 | Mean metrics in 5 fold: 62 | RPITER : [0.9682369545081408, 0.985610872193569, 0.9463753387533875, 0.9586180819859658, 0.935934447162937, 0.9896721625933399] 63 | Conjoint-Struct-SAE : [0.9657640200352064, 0.9795182198007376, 0.9484538327526133, 0.9600397323716452, 0.9309744723870856, 0.9888107324876717] 64 | Conjoint-CNN : [0.9670004872716736, 0.980070705988583, 0.9505516840882695, 0.9615666948156795, 0.9333337015395836, 0.9890253617249861] 65 | Conjoint-Struct-CNN : [0.9654643991706138, 0.9784163082903536, 0.9491652148664345, 0.9604512537001361, 0.9301915078997591, 0.9889033189787282] 66 | Conjoint-SAE : [0.9629919402631266, 0.9773036837513965, 0.944981610530391, 0.9573281718930403, 0.9252874688038798, 0.9886587452553106] 67 | -------------------------------------------------------------------------------- /result/NPInter/NPInter-18-07-21-11-36-33/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set NPInter 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [35, 35, 35, 35, 15], 13 | SECOND_TRAIN_EPOCHS = [15, 15, 15, 15, 15], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | CODING_FREQUENCY = True, 18 | MONITOR = acc, 19 | MIN_DELTA = 0.0, 20 | 21 | Pos samples: 10412, Neg samples: 10412. 22 | 23 | 24 | K-fold cross validation processes: 25 | 26 | # ========== Fold 1 ========== 27 | RPITER : [0.9505520883341335, 0.9731156985117618, 0.9279884781565051, 0.9310978410656867, 0.9020231144626678, 0.9829702949303382] 28 | Conjoint-Struct-SAE : [0.9397503600576093, 0.9659145463274124, 0.913586173787806, 0.9178832116788321, 0.8807073489209595, 0.9785601798063974] 29 | Conjoint-CNN : [0.9507921267402785, 0.9659145463274124, 0.9356697071531445, 0.9375582479030755, 0.9019968988344177, 0.9825643154272903] 30 | Conjoint-Struct-CNN : [0.9500720115218435, 0.9687950072011522, 0.9313490158425348, 0.933826931975937, 0.9007757795048253, 0.9823239313105268] 31 | Conjoint-SAE : [0.9385501680268843, 0.957273163706193, 0.9198271723475756, 0.922720962517353, 0.8777159195495018, 0.9790442899092372] 32 | 33 | # ========== Fold 2 ========== 34 | RPITER : [0.9589534325492078, 0.9774363898223716, 0.9404704752760442, 0.9425925925925925, 0.91853465872508, 0.9864125355592182] 35 | Conjoint-Struct-SAE : [0.943590974555929, 0.9663946231397024, 0.9207873259721555, 0.9242424242424242, 0.8881060715737717, 0.9822754165870572] 36 | Conjoint-CNN : [0.9584733557369179, 0.9774363898223716, 0.9395103216514642, 0.9417206290471786, 0.9176068857014629, 0.9847126764478188] 37 | Conjoint-Struct-CNN : [0.9551128180508881, 0.9764762361977917, 0.9337493999039846, 0.93646408839779, 0.9110576214328723, 0.9859832782078547] 38 | Conjoint-SAE : [0.9455112818050888, 0.970715314450312, 0.9203072491598656, 0.9241316270566727, 0.8921567576947955, 0.9826588096630265] 39 | 40 | # ========== Fold 3 ========== 41 | RPITER : [0.9522094140249759, 0.9702209414024976, 0.9341978866474544, 0.9364858599907279, 0.9050062140650214, 0.9833333102638139] 42 | Conjoint-Struct-SAE : [0.9378001921229587, 0.9707012487992315, 0.9048991354466859, 0.910770617395223, 0.8775022014995149, 0.9803228533119985] 43 | Conjoint-CNN : [0.946445725264169, 0.9678194044188281, 0.9250720461095101, 0.9281437125748503, 0.8937083769312766, 0.9818409430450473] 44 | Conjoint-Struct-CNN : [0.9481268011527377, 0.9707012487992315, 0.925552353506244, 0.9287683823529411, 0.8971684730779936, 0.9796729849466772] 45 | Conjoint-SAE : [0.9423631123919308, 0.9663784822286263, 0.9183477425552353, 0.922089825847846, 0.8857485043329653, 0.9810329331233085] 46 | 47 | # ========== Fold 4 ========== 48 | RPITER : [0.9534101825168108, 0.962536023054755, 0.9442843419788665, 0.9452830188679245, 0.9069714445319225, 0.9849541747063942] 49 | Conjoint-Struct-SAE : [0.9423631123919308, 0.9711815561959655, 0.9135446685878963, 0.9182561307901907, 0.8861994313504855, 0.9823758098554833] 50 | Conjoint-CNN : [0.9548511047070125, 0.9764649375600384, 0.9332372718539865, 0.9360036832412523, 0.9105533513647944, 0.984512393407285] 51 | Conjoint-Struct-CNN : [0.9526897214217099, 0.9711815561959655, 0.9341978866474544, 0.936544696618805, 0.9059992640506375, 0.9835062163127342] 52 | Conjoint-SAE : [0.9409221902017291, 0.9687800192122958, 0.9130643611911623, 0.9176524112829846, 0.883216301200082, 0.9821135094183621] 53 | 54 | # ========== Fold 5 ========== 55 | RPITER : [0.9594140249759846, 0.9812680115273775, 0.9375600384245918, 0.9401748734468477, 0.919706967998641, 0.9854988460626328] 56 | Conjoint-Struct-SAE : [0.9452449567723343, 0.9702209414024976, 0.920268972142171, 0.9240622140896615, 0.8916029718295926, 0.9824330222639319] 57 | Conjoint-CNN : [0.9555715658021133, 0.9759846301633045, 0.9351585014409222, 0.9377018920166128, 0.9119034164410669, 0.9856464909876615] 58 | Conjoint-Struct-CNN : [0.957492795389049, 0.978386167146974, 0.9365994236311239, 0.9391424619640387, 0.9157854812458239, 0.9860945010570452] 59 | Conjoint-SAE : [0.9423631123919308, 0.962536023054755, 0.9221902017291066, 0.925207756232687, 0.8854471772411349, 0.9830983472073423] 60 | 61 | Mean metrics in 5 fold: 62 | RPITER : [0.9549078284802224, 0.9729154128637527, 0.9369002440966924, 0.9391268371927559, 0.9104484799566664, 0.9846338323044794] 63 | Conjoint-Struct-SAE : [0.9417499191801524, 0.9688825831729618, 0.914617255187343, 0.9190429196392664, 0.8848236050348648, 0.9811934563649736] 64 | Conjoint-CNN : [0.9532267756500982, 0.972723981658391, 0.9337295696418055, 0.9362256329565939, 0.9071537858546037, 0.9838553638630205] 65 | Conjoint-Struct-CNN : [0.9526988295072456, 0.973108043108223, 0.9322896159062684, 0.9349493122619024, 0.9061573238624305, 0.9835161823669676] 66 | Conjoint-SAE : [0.9419419729635127, 0.9651366005304365, 0.9187473453965891, 0.9223605165875087, 0.8848569320036959, 0.9815895778642554] 67 | -------------------------------------------------------------------------------- /result/RPI1807/RPI1807-20-04-29-08-22-51/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI1807 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [25, 25, 25, 25, 15], 13 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | SGD_LEARNING_RATE = 0.005, 18 | ADAM_LEARNING_RATE = 0.001, 19 | FREEZE_SUB_MODELS = True, 20 | CODING_FREQUENCY = True, 21 | MONITOR = acc, 22 | MIN_DELTA = 0.0, 23 | 24 | Pos samples: 1801, Neg samples: 1436. 25 | 26 | 27 | K-fold cross validation processes: 28 | 29 | # ========== Fold 0 ========== 30 | RPITER : [0.9691358024691358, 0.9833795013850416, 0.9512195121951219, 0.962059620596206, 0.9375719006543805, 0.9923267732875192] 31 | Conjoint-SAE : [0.9691358024691358, 0.9806094182825484, 0.9547038327526133, 0.9645776566757494, 0.9374874108957858, 0.9899813719150251] 32 | Conjoint-Struct-SAE : [0.9675925925925926, 0.9806094182825484, 0.9512195121951219, 0.9619565217391305, 0.9343892088183875, 0.9890354898800274] 33 | Conjoint-CNN : [0.9722222222222222, 0.9861495844875346, 0.9547038327526133, 0.964769647696477, 0.9438461990879816, 0.9883791635700291] 34 | Conjoint-Struct-CNN : [0.9722222222222222, 0.9833795013850416, 0.9581881533101045, 0.9673024523160763, 0.9437563530789597, 0.9917959211250206] 35 | 36 | # ========== Fold 1 ========== 37 | RPITER : [0.9660493827160493, 0.9861111111111112, 0.9409722222222222, 0.9543010752688172, 0.9316231179726565, 0.9891396604938272] 38 | Conjoint-SAE : [0.9675925925925926, 0.9861111111111112, 0.9444444444444444, 0.9568733153638814, 0.9346800243323886, 0.9895206404320988] 39 | Conjoint-Struct-SAE : [0.9660493827160493, 0.9888888888888889, 0.9375, 0.9518716577540107, 0.9318151114927915, 0.990504436728395] 40 | Conjoint-CNN : [0.9660493827160493, 0.9888888888888889, 0.9375, 0.9518716577540107, 0.9318151114927915, 0.988064236111111] 41 | Conjoint-Struct-CNN : [0.9645061728395061, 0.9833333333333333, 0.9409722222222222, 0.954177897574124, 0.9284023226764249, 0.9856626157407408] 42 | 43 | # ========== Fold 2 ========== 44 | RPITER : [0.9752704791344667, 0.9916666666666667, 0.9547038327526133, 0.9648648648648649, 0.9501947926823044, 0.99455574912892] 45 | Conjoint-SAE : [0.9690880989180835, 0.975, 0.9616724738675958, 0.9696132596685083, 0.9373531457646107, 0.9934330236159504] 46 | Conjoint-Struct-SAE : [0.9675425038639877, 0.975, 0.9581881533101045, 0.9669421487603306, 0.9342195106857966, 0.9919618660472319] 47 | Conjoint-CNN : [0.9752704791344667, 0.9888888888888889, 0.9581881533101045, 0.967391304347826, 0.9500610136676539, 0.9924167634533488] 48 | Conjoint-Struct-CNN : [0.9690880989180835, 0.9805555555555555, 0.9547038327526133, 0.9644808743169399, 0.9374121377342611, 0.9917102206736353] 49 | 50 | # ========== Fold 3 ========== 51 | RPITER : [0.9783616692426584, 0.9916666666666667, 0.9616724738675958, 0.970108695652174, 0.9563428421008282, 0.992324816105304] 52 | Conjoint-SAE : [0.9675425038639877, 0.975, 0.9581881533101045, 0.9669421487603306, 0.9342195106857966, 0.991337591947348] 53 | Conjoint-Struct-SAE : [0.9675425038639877, 0.9722222222222222, 0.9616724738675958, 0.9695290858725761, 0.9342293135514254, 0.9918408826945412] 54 | Conjoint-CNN : [0.9721792890262752, 0.9833333333333333, 0.9581881533101045, 0.9672131147540983, 0.9436886499623667, 0.9922183507549361] 55 | Conjoint-Struct-CNN : [0.9752704791344667, 0.9833333333333333, 0.9651567944250871, 0.9725274725274725, 0.9499070351016404, 0.9936314363143631] 56 | 57 | # ========== Fold 4 ========== 58 | RPITER : [0.9675425038639877, 0.9777777777777777, 0.9547038327526133, 0.9643835616438357, 0.9342465187029276, 0.9938346883468835] 59 | Conjoint-SAE : [0.9721792890262752, 0.9861111111111112, 0.9547038327526133, 0.9646739130434783, 0.9437791852344797, 0.9918844367015098] 60 | Conjoint-Struct-SAE : [0.973724884080371, 0.9888888888888889, 0.9547038327526133, 0.964769647696477, 0.9469808572164214, 0.9894405729771584] 61 | Conjoint-CNN : [0.9613601236476044, 0.9666666666666667, 0.9547038327526133, 0.96398891966759, 0.9217006294210953, 0.9920199380565233] 62 | Conjoint-Struct-CNN : [0.964451313755796, 0.9777777777777777, 0.9477351916376306, 0.9591280653950953, 0.928031376708723, 0.9914634146341463] 63 | 64 | Mean metrics in 5 fold: 65 | RPITER : [0.9712719674852597, 0.9861203447214528, 0.9526543747580334, 0.9631435636051794, 0.9419958344226196, 0.9924363374724907] 66 | Conjoint-SAE : [0.9691076573740149, 0.9805663281009542, 0.9547425474254743, 0.9645360587023897, 0.9375038553826123, 0.9912314129223866] 67 | Conjoint-Struct-SAE : [0.9684903734233977, 0.9811218836565097, 0.9526567944250871, 0.963013812364505, 0.9363268003529646, 0.9905566496654709] 68 | Conjoint-CNN : [0.9694162993493235, 0.9827854724530625, 0.9526567944250871, 0.9630469288440004, 0.9382223207263778, 0.9906196903891896] 69 | Conjoint-Struct-CNN : [0.9691076573740147, 0.9816759002770082, 0.9533512388695315, 0.9635233524259416, 0.9375018450600019, 0.9908527216975813] 70 | -------------------------------------------------------------------------------- /result/RPI369/RPI369-20-04-29-08-22-51/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI369 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 80, 12 | FIRST_TRAIN_EPOCHS = [20, 20, 20, 20, 10], 13 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 15], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | SGD_LEARNING_RATE = 0.01, 18 | ADAM_LEARNING_RATE = 0.001, 19 | FREEZE_SUB_MODELS = False, 20 | CODING_FREQUENCY = True, 21 | MONITOR = acc, 22 | MIN_DELTA = 0.0, 23 | 24 | Pos samples: 369, Neg samples: 369. 25 | 26 | 27 | K-fold cross validation processes: 28 | 29 | # ========== Fold 0 ========== 30 | RPITER : [0.7635135135135135, 0.7837837837837838, 0.7432432432432432, 0.7532467532467533, 0.5274606554080364, 0.837472607742878] 31 | Conjoint-SAE : [0.7567567567567568, 0.8513513513513513, 0.6621621621621622, 0.7159090909090909, 0.5229577884350212, 0.8308984660336011] 32 | Conjoint-Struct-SAE : [0.7162162162162162, 0.7972972972972973, 0.6351351351351351, 0.686046511627907, 0.4382328285318214, 0.8113586559532505] 33 | Conjoint-CNN : [0.7432432432432432, 0.7837837837837838, 0.7027027027027027, 0.725, 0.4880935300919764, 0.8155588020452885] 34 | Conjoint-Struct-CNN : [0.6959459459459459, 0.7432432432432432, 0.6486486486486487, 0.6790123456790124, 0.39365709848142794, 0.7678962746530315] 35 | 36 | # ========== Fold 1 ========== 37 | RPITER : [0.7027027027027027, 0.8108108108108109, 0.5945945945945946, 0.6666666666666666, 0.4152273992686999, 0.8210372534696859] 38 | Conjoint-SAE : [0.7094594594594594, 0.8513513513513513, 0.5675675675675675, 0.6631578947368421, 0.43687979162884655, 0.8173849525200876] 39 | Conjoint-Struct-SAE : [0.6824324324324325, 0.8378378378378378, 0.527027027027027, 0.6391752577319587, 0.38387763362632177, 0.7925493060628196] 40 | Conjoint-CNN : [0.7162162162162162, 0.7432432432432432, 0.6891891891891891, 0.7051282051282052, 0.4330655695686134, 0.7806793279766252] 41 | Conjoint-Struct-CNN : [0.7027027027027027, 0.7162162162162162, 0.6891891891891891, 0.6973684210526315, 0.4055535528269063, 0.7804967129291455] 42 | 43 | # ========== Fold 2 ========== 44 | RPITER : [0.75, 0.7972972972972973, 0.7027027027027027, 0.7283950617283951, 0.502252160131477, 0.8184806428049672] 45 | Conjoint-SAE : [0.6824324324324325, 0.8243243243243243, 0.5405405405405406, 0.6421052631578947, 0.38050820561222115, 0.7825054784514245] 46 | Conjoint-Struct-SAE : [0.722972972972973, 0.8378378378378378, 0.6081081081081081, 0.6813186813186813, 0.458200771267413, 0.800219138056976] 47 | Conjoint-CNN : [0.6824324324324325, 0.6891891891891891, 0.6756756756756757, 0.68, 0.3648981843357224, 0.7699050401753105] 48 | Conjoint-Struct-CNN : [0.7162162162162162, 0.7432432432432432, 0.6891891891891891, 0.7051282051282052, 0.4330655695686134, 0.7976625273922572] 49 | 50 | # ========== Fold 3 ========== 51 | RPITER : [0.6802721088435374, 0.7837837837837838, 0.5753424657534246, 0.651685393258427, 0.3673799408540411, 0.7852647167715661] 52 | Conjoint-SAE : [0.6938775510204082, 0.8108108108108109, 0.5753424657534246, 0.6593406593406593, 0.3975779632164742, 0.7563865235098113] 53 | Conjoint-Struct-SAE : [0.6938775510204082, 0.8378378378378378, 0.547945205479452, 0.6526315789473685, 0.40341955894718357, 0.8076638282117734] 54 | Conjoint-CNN : [0.673469387755102, 0.7162162162162162, 0.6301369863013698, 0.6625, 0.3477075354477737, 0.7645316549426138] 55 | Conjoint-Struct-CNN : [0.7006802721088435, 0.7027027027027027, 0.6986301369863014, 0.7027027027027027, 0.4013328396890041, 0.7388004442798962] 56 | 57 | # ========== Fold 4 ========== 58 | RPITER : [0.7482993197278912, 0.8356164383561644, 0.6621621621621622, 0.7093023255813954, 0.5051255881995507, 0.8474639022584227] 59 | Conjoint-SAE : [0.7006802721088435, 0.8767123287671232, 0.527027027027027, 0.6464646464646465, 0.4304672934102268, 0.8004442798963347] 60 | Conjoint-Struct-SAE : [0.7278911564625851, 0.863013698630137, 0.5945945945945946, 0.6774193548387096, 0.47460556069439636, 0.8215475749722325] 61 | Conjoint-CNN : [0.7346938775510204, 0.726027397260274, 0.7432432432432432, 0.7361111111111112, 0.4693575344289918, 0.8200666419844503] 62 | Conjoint-Struct-CNN : [0.7210884353741497, 0.7397260273972602, 0.7027027027027027, 0.7105263157894737, 0.4426746377039983, 0.8196964087375046] 63 | 64 | Mean metrics in 5 fold: 65 | RPITER : [0.728957528957529, 0.802258422806368, 0.6556090336912255, 0.7018592400963274, 0.463489148772361, 0.821943824609504] 66 | Conjoint-SAE : [0.7086412943555801, 0.8429100333209923, 0.5745279526101443, 0.6653955109218267, 0.433678208460558, 0.7975239400822519] 67 | Conjoint-Struct-SAE : [0.7086780658209231, 0.8347649018881895, 0.5825620140688634, 0.6673182768929251, 0.43166727061342725, 0.8066677006514104] 68 | Conjoint-CNN : [0.7100110314396029, 0.7316919659385412, 0.6881895594224361, 0.7017478632478633, 0.42062447077461557, 0.7901482934248577] 69 | Conjoint-Struct-CNN : [0.7073267144695716, 0.729026286560533, 0.6856719733432062, 0.698947598070405, 0.41525673965399, 0.780910473598367] 70 | -------------------------------------------------------------------------------- /result/RPI2241/RPI2241-20-04-29-08-22-51/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI2241 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [35, 35, 35, 35, 15], 13 | SECOND_TRAIN_EPOCHS = [10, 10, 10, 10, 10], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | SGD_LEARNING_RATE = 0.01, 18 | ADAM_LEARNING_RATE = 0.001, 19 | FREEZE_SUB_MODELS = False, 20 | CODING_FREQUENCY = True, 21 | MONITOR = acc, 22 | MIN_DELTA = 0.0, 23 | 24 | Pos samples: 2241, Neg samples: 2241. 25 | 26 | 27 | K-fold cross validation processes: 28 | 29 | # ========== Fold 0 ========== 30 | RPITER : [0.8695652173913043, 0.8930957683741648, 0.8459821428571429, 0.8531914893617021, 0.7399281222760482, 0.944609051861279] 31 | Conjoint-SAE : [0.8416945373467113, 0.8930957683741648, 0.7901785714285714, 0.8101010101010101, 0.6869761546465466, 0.9285813713013047] 32 | Conjoint-Struct-SAE : [0.830546265328874, 0.8930957683741648, 0.7678571428571429, 0.7940594059405941, 0.6662603594005205, 0.9208956411072223] 33 | Conjoint-CNN : [0.8595317725752508, 0.8864142538975501, 0.8325892857142857, 0.8414376321353065, 0.7200782732083035, 0.9436346643334392] 34 | Conjoint-Struct-CNN : [0.867335562987737, 0.9042316258351893, 0.8303571428571429, 0.8423236514522822, 0.7366461002161921, 0.9390013522112632] 35 | 36 | # ========== Fold 1 ========== 37 | RPITER : [0.8851727982162765, 0.9151785714285714, 0.8552338530066815, 0.8631578947368421, 0.7717602838435582, 0.9499880687241488] 38 | Conjoint-SAE : [0.8717948717948718, 0.9151785714285714, 0.8285077951002228, 0.8418891170431212, 0.7464411761023242, 0.9408556713331212] 39 | Conjoint-Struct-SAE : [0.8517279821627648, 0.8995535714285714, 0.8040089086859689, 0.8207739307535642, 0.706742300465633, 0.9355909958638244] 40 | Conjoint-CNN : [0.887402452619844, 0.8973214285714286, 0.8775055679287305, 0.8796498905908097, 0.7749657038572558, 0.9511215399300033] 41 | Conjoint-Struct-CNN : [0.8784838350055741, 0.8973214285714286, 0.8596881959910914, 0.864516129032258, 0.7575219626834562, 0.9460507476932867] 42 | 43 | # ========== Fold 2 ========== 44 | RPITER : [0.8761160714285714, 0.9017857142857143, 0.8504464285714286, 0.8577494692144374, 0.7532254443400388, 0.9498565051020409] 45 | Conjoint-SAE : [0.8359375, 0.8839285714285714, 0.7879464285714286, 0.8065173116089613, 0.6749913974551863, 0.929169323979592] 46 | Conjoint-Struct-SAE : [0.8392857142857143, 0.8861607142857143, 0.7924107142857143, 0.810204081632653, 0.6815732337664311, 0.9305843431122449] 47 | Conjoint-CNN : [0.8604910714285714, 0.8995535714285714, 0.8214285714285714, 0.8343685300207039, 0.7231925292524286, 0.9403823541135203] 48 | Conjoint-Struct-CNN : [0.8705357142857143, 0.8995535714285714, 0.8415178571428571, 0.8502109704641351, 0.7423226077659496, 0.9482870296556123] 49 | 50 | # ========== Fold 3 ========== 51 | RPITER : [0.8816964285714286, 0.9040178571428571, 0.859375, 0.8653846153846154, 0.764154711244699, 0.9560048628826532] 52 | Conjoint-SAE : [0.8571428571428571, 0.9263392857142857, 0.7879464285714286, 0.8137254901960784, 0.7212257731592414, 0.9394630899234693] 53 | Conjoint-Struct-SAE : [0.8571428571428571, 0.9196428571428571, 0.7946428571428571, 0.8174603174603174, 0.7199323295413852, 0.9455167809311225] 54 | Conjoint-CNN : [0.8761160714285714, 0.90625, 0.8459821428571429, 0.8547368421052631, 0.7536020100150261, 0.9499760841836735] 55 | Conjoint-Struct-CNN : [0.8794642857142857, 0.8950892857142857, 0.8638392857142857, 0.8679653679653679, 0.7592994136551628, 0.9566974250637755] 56 | 57 | # ========== Fold 4 ========== 58 | RPITER : [0.8783482142857143, 0.9241071428571429, 0.8325892857142857, 0.8466257668711656, 0.7598853365245236, 0.9431152343750001] 59 | Conjoint-SAE : [0.8448660714285714, 0.8727678571428571, 0.8169642857142857, 0.8266384778012685, 0.690808583822396, 0.9292639907525511] 60 | Conjoint-Struct-SAE : [0.8381696428571429, 0.8839285714285714, 0.7924107142857143, 0.8098159509202454, 0.6791895485750167, 0.9262346540178572] 61 | Conjoint-CNN : [0.8716517857142857, 0.9017857142857143, 0.8415178571428571, 0.8505263157894737, 0.7446571790356192, 0.9355518574617346] 62 | Conjoint-Struct-CNN : [0.8705357142857143, 0.8995535714285714, 0.8415178571428571, 0.8502109704641351, 0.7423226077659496, 0.9344382772640307] 63 | 64 | Mean metrics in 5 fold: 65 | RPITER : [0.878179745978659, 0.9076370108176901, 0.8487253420299078, 0.8572218471137525, 0.7577907796457735, 0.9487147445890244] 66 | Conjoint-SAE : [0.8502871675426024, 0.89826201081769, 0.8023087018771875, 0.8197742813500879, 0.704088617037139, 0.9334666894580076] 67 | Conjoint-Struct-SAE : [0.8433744923554707, 0.8964762965319759, 0.7902660674514795, 0.8104627373414747, 0.6907395543497973, 0.9317644830064543] 68 | Conjoint-CNN : [0.8710386307533046, 0.8982649936366529, 0.8438046850143175, 0.8521438421283115, 0.7432991390737267, 0.9441333000044743] 69 | Conjoint-Struct-CNN : [0.8732710224558051, 0.8991498965956092, 0.8473840677696469, 0.8550454178756356, 0.7476225384173422, 0.9448949663775936] 70 | -------------------------------------------------------------------------------- /result/RPI488/RPI488-20-04-29-08-23-32/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set RPI488 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 80, 12 | FIRST_TRAIN_EPOCHS = [3, 3, 6, 6, 3], 13 | SECOND_TRAIN_EPOCHS = [3, 3, 6, 6, 5], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | SGD_LEARNING_RATE = 0.001, 18 | ADAM_LEARNING_RATE = 0.001, 19 | FREEZE_SUB_MODELS = True, 20 | CODING_FREQUENCY = True, 21 | MONITOR = acc, 22 | MIN_DELTA = 0.0, 23 | 24 | Pos samples: 243, Neg samples: 245. 25 | 26 | 27 | K-fold cross validation processes: 28 | 29 | # ========== Fold 0 ========== 30 | RPITER : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.8971261974177426] 31 | Conjoint-SAE : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.882548937942524] 32 | Conjoint-Struct-SAE : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.8908788004997917] 33 | Conjoint-CNN : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.8804664723032071] 34 | Conjoint-Struct-CNN : [0.8571428571428571, 0.8367346938775511, 0.8775510204081632, 0.8723404255319149, 0.7148814489299585, 0.9042065805914202] 35 | 36 | # ========== Fold 1 ========== 37 | RPITER : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9429404414827155] 38 | Conjoint-SAE : [0.8979591836734694, 0.8571428571428571, 0.9387755102040817, 0.9333333333333333, 0.7985836518841366, 0.9321116201582674] 39 | Conjoint-Struct-SAE : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9337775926697209] 40 | Conjoint-CNN : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9358600583090378] 41 | Conjoint-Struct-CNN : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9550187421907539] 42 | 43 | # ========== Fold 2 ========== 44 | RPITER : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9433569346105789] 45 | Conjoint-SAE : [0.9183673469387755, 0.8571428571428571, 0.9795918367346939, 0.9767441860465116, 0.8430790241814816, 0.9504373177842566] 46 | Conjoint-Struct-SAE : [0.9285714285714286, 0.8775510204081632, 0.9795918367346939, 0.9772727272727273, 0.8616404368553291, 0.9496043315285299] 47 | Conjoint-CNN : [0.9081632653061225, 0.8367346938775511, 0.9795918367346939, 0.9761904761904762, 0.8247860988423226, 0.9421074552269888] 48 | Conjoint-Struct-CNN : [0.9081632653061225, 0.8571428571428571, 0.9591836734693877, 0.9545454545454546, 0.8206099398622181, 0.9379425239483548] 49 | 50 | # ========== Fold 3 ========== 51 | RPITER : [0.8556701030927835, 0.7916666666666666, 0.9183673469387755, 0.9047619047619048, 0.7164597917626588, 0.8958333333333333] 52 | Conjoint-SAE : [0.865979381443299, 0.7916666666666666, 0.9387755102040817, 0.926829268292683, 0.7392963447134014, 0.8958333333333333] 53 | Conjoint-Struct-SAE : [0.865979381443299, 0.7916666666666666, 0.9387755102040817, 0.926829268292683, 0.7392963447134014, 0.8809523809523809] 54 | Conjoint-CNN : [0.865979381443299, 0.7916666666666666, 0.9387755102040817, 0.926829268292683, 0.7392963447134014, 0.8988095238095238] 55 | Conjoint-Struct-CNN : [0.865979381443299, 0.8125, 0.9183673469387755, 0.9069767441860465, 0.735573560066312, 0.903061224489796] 56 | 57 | # ========== Fold 4 ========== 58 | RPITER : [0.9072164948453608, 0.8333333333333334, 0.9795918367346939, 0.975609756097561, 0.8227791682724235, 0.9226190476190476] 59 | Conjoint-SAE : [0.9072164948453608, 0.8333333333333334, 0.9795918367346939, 0.975609756097561, 0.8227791682724235, 0.9281462585034014] 60 | Conjoint-Struct-SAE : [0.9072164948453608, 0.8333333333333334, 0.9795918367346939, 0.975609756097561, 0.8227791682724235, 0.9153911564625852] 61 | Conjoint-CNN : [0.8865979381443299, 0.7916666666666666, 0.9795918367346939, 0.9743589743589743, 0.7864521856445373, 0.9047619047619047] 62 | Conjoint-Struct-CNN : [0.8865979381443299, 0.7916666666666666, 0.9795918367346939, 0.9743589743589743, 0.7864521856445373, 0.9149659863945578] 63 | 64 | Mean metrics in 5 fold: 65 | RPITER : [0.8872711971386493, 0.835204081632653, 0.9387755102040816, 0.9323605990964581, 0.7790680577378953, 0.9203751908926835] 66 | Conjoint-SAE : [0.8893330528087524, 0.835204081632653, 0.9428571428571428, 0.9369713938604007, 0.7837239275962803, 0.9178154935443565] 67 | Conjoint-Struct-SAE : [0.8934146854618137, 0.8392857142857142, 0.9469387755102041, 0.941319526348068, 0.7918414677266661, 0.9141208524226017] 68 | Conjoint-CNN : [0.8852093414685462, 0.8227891156462585, 0.9469387755102041, 0.9408529197839005, 0.7772052035984875, 0.9124010828821323] 69 | Conjoint-Struct-CNN : [0.8852093414685462, 0.8310374149659865, 0.9387755102040816, 0.932553410633569, 0.7756254148730488, 0.9230390115229765] 70 | -------------------------------------------------------------------------------- /result/NPInter/NPInter-20-04-29-09-29-48/result.txt: -------------------------------------------------------------------------------- 1 | # Analyze data set NPInter 2 | 3 | Program parameters: 4 | WINDOW_P_UPLIMIT = 3, 5 | WINDOW_R_UPLIMIT = 4, 6 | WINDOW_P_STRUCT_UPLIMIT = 3, 7 | WINDOW_R_STRUCT_UPLIMIT = 4, 8 | VECTOR_REPETITION_CNN = 1, 9 | RANDOM_SEED = 1, 10 | K_FOLD = 5, 11 | BATCH_SIZE = 150, 12 | FIRST_TRAIN_EPOCHS = [35, 35, 35, 35, 15], 13 | SECOND_TRAIN_EPOCHS = [15, 15, 15, 15, 15], 14 | PATIENCES = [10, 10, 10, 10, 10], 15 | FIRST_OPTIMIZER = adam, 16 | SECOND_OPTIMIZER = sgd, 17 | SGD_LEARNING_RATE = 0.005, 18 | ADAM_LEARNING_RATE = 0.001, 19 | FREEZE_SUB_MODELS = False, 20 | CODING_FREQUENCY = True, 21 | MONITOR = acc, 22 | MIN_DELTA = 0.0, 23 | 24 | Pos samples: 10412, Neg samples: 10412. 25 | 26 | 27 | K-fold cross validation processes: 28 | 29 | # ========== Fold 0 ========== 30 | RPITER : [0.954141656662665, 0.9673547767642823, 0.9409221902017291, 0.9424695977549111, 0.9085996680535798, 0.9853998541783976] 31 | Conjoint-SAE : [0.9366146458583433, 0.9721555448871819, 0.9010566762728146, 0.9076647243388615, 0.8754407995345855, 0.9787481847239651] 32 | Conjoint-Struct-SAE : [0.9361344537815126, 0.9611137782045127, 0.9111431316042267, 0.9154092363968908, 0.8733571516922262, 0.9795687194677373] 33 | Conjoint-CNN : [0.9510204081632653, 0.9678348535765723, 0.9341978866474544, 0.9363678588016721, 0.9025500517713975, 0.9852996652375043] 34 | Conjoint-Struct-CNN : [0.9505402160864346, 0.971195391262602, 0.9298751200768491, 0.9326878745965883, 0.9018487967319345, 0.9842325204309347] 35 | 36 | # ========== Fold 1 ========== 37 | RPITER : [0.9572629051620648, 0.970715314450312, 0.9438040345821326, 0.9453015427769986, 0.9148560897323411, 0.9859910726926683] 38 | Conjoint-SAE : [0.9409363745498199, 0.9654344695151225, 0.9164265129682997, 0.9203661327231121, 0.8829310901522073, 0.9823485302316958] 39 | Conjoint-Struct-SAE : [0.9406962785114046, 0.9668746999519923, 0.914505283381364, 0.9187956204379562, 0.8826008944315525, 0.9811455711876436] 40 | Conjoint-CNN : [0.9519807923169268, 0.971195391262602, 0.9327569644572526, 0.9352750809061489, 0.9046284504915749, 0.9847695516008786] 41 | Conjoint-Struct-CNN : [0.954141656662665, 0.970715314450312, 0.9375600384245918, 0.9395910780669146, 0.9087815592581499, 0.9856027684890677] 42 | 43 | # ========== Fold 2 ========== 44 | RPITER : [0.9512605042016806, 0.9740634005763689, 0.928468554968795, 0.9315571887919155, 0.9034626034151088, 0.9832647575197045] 45 | Conjoint-SAE : [0.9457382953181273, 0.9707012487992315, 0.9207873259721555, 0.92451967063129, 0.8925916161495904, 0.9813249658850314] 46 | Conjoint-Struct-SAE : [0.946218487394958, 0.9750240153698367, 0.9174267882861258, 0.92188919164396, 0.893923749026084, 0.9821709802098595] 47 | Conjoint-CNN : [0.9512605042016806, 0.9707012487992315, 0.9318290926548247, 0.9343504392048081, 0.903205372655994, 0.9819194125815175] 48 | Conjoint-Struct-CNN : [0.9481392557022809, 0.9702209414024976, 0.9260681709073452, 0.9291628334866605, 0.8971554888732748, 0.9817932828906802] 49 | 50 | # ========== Fold 3 ========== 51 | RPITER : [0.9527010804321728, 0.9764649375600384, 0.928948631781085, 0.9321412196240256, 0.906428035832824, 0.9847620576064504] 52 | Conjoint-SAE : [0.9440576230492197, 0.9769452449567724, 0.9111857897263562, 0.9166291122127084, 0.8900450203099977, 0.9803885624581778] 53 | Conjoint-Struct-SAE : [0.9440576230492197, 0.9807877041306436, 0.9073451752280365, 0.9136465324384787, 0.8905237933466988, 0.9827300321942001] 54 | Conjoint-CNN : [0.9531812725090036, 0.9759846301633045, 0.9303888622179549, 0.9333945796968305, 0.9073081014606128, 0.9827032843987026] 55 | Conjoint-Struct-CNN : [0.9512605042016806, 0.9707012487992315, 0.9318290926548247, 0.9343504392048081, 0.903205372655994, 0.9829358288104195] 56 | 57 | # ========== Fold 4 ========== 58 | RPITER : [0.9512487992315082, 0.9500480307396734, 0.952449567723343, 0.9523350987000482, 0.902500200997521, 0.9836903110786294] 59 | Conjoint-SAE : [0.94164265129683, 0.9649375600384246, 0.9183477425552353, 0.9219825608077099, 0.8842455004769499, 0.9834056332075584] 60 | Conjoint-Struct-SAE : [0.9406820365033621, 0.9707012487992315, 0.9106628242074928, 0.9157227004984141, 0.882956868305166, 0.9818062234181462] 61 | Conjoint-CNN : [0.9517291066282421, 0.9707012487992315, 0.9327569644572526, 0.93521517815826, 0.9041093018386552, 0.9821444225745399] 62 | Conjoint-Struct-CNN : [0.9536503362151777, 0.9731027857829011, 0.9341978866474544, 0.93666204345816, 0.907988094031531, 0.9844301505701402] 63 | 64 | Mean metrics in 5 fold: 65 | RPITER : [0.9533229891380184, 0.9677292920181351, 0.9389185958514169, 0.9407609295295798, 0.9071693196062748, 0.9846216106151701] 66 | Conjoint-SAE : [0.941797918014468, 0.9700348136393465, 0.9135608094989724, 0.9182324401427364, 0.8850508053246662, 0.9812431753012858] 67 | Conjoint-Struct-SAE : [0.9415577758480914, 0.9709002892912434, 0.9122166405414491, 0.9170926562831399, 0.8846724913603454, 0.9814843052955172] 68 | Conjoint-CNN : [0.9518344167638236, 0.9712834745201884, 0.9323859540869478, 0.934920627353544, 0.9043602556436469, 0.9833672672786286] 69 | Conjoint-Struct-CNN : [0.9515463937736479, 0.9711871363395088, 0.931906061742213, 0.9344908537626262, 0.903795862310177, 0.9837989102382485] 70 | -------------------------------------------------------------------------------- /sample/utils/basic_modules.py: -------------------------------------------------------------------------------- 1 | from keras.layers import Dense, Conv1D, concatenate, BatchNormalization, MaxPooling1D 2 | from keras.layers import Dropout, Flatten, Input 3 | from keras.models import Model 4 | 5 | 6 | def conjoint_cnn(pro_coding_length, rna_coding_length, vector_repeatition_cnn): 7 | 8 | if type(vector_repeatition_cnn)==int: 9 | vec_len_p = vector_repeatition_cnn 10 | vec_len_r = vector_repeatition_cnn 11 | else: 12 | vec_len_p = vector_repeatition_cnn[0] 13 | vec_len_r = vector_repeatition_cnn[1] 14 | 15 | # NN for protein feature analysis by one hot encoding 16 | xp_in_conjoint_cnn = Input(shape=(pro_coding_length, vec_len_p)) 17 | xp_cnn = Conv1D(filters=45, kernel_size=6, strides=1, activation='relu')(xp_in_conjoint_cnn) 18 | xp_cnn = MaxPooling1D(pool_size=2)(xp_cnn) 19 | xp_cnn = BatchNormalization()(xp_cnn) 20 | xp_cnn = Dropout(0.2)(xp_cnn) 21 | xp_cnn = Conv1D(filters=64, kernel_size=5, strides=1, activation='relu')(xp_cnn) 22 | xp_cnn = MaxPooling1D(pool_size=2)(xp_cnn) 23 | xp_cnn = BatchNormalization()(xp_cnn) 24 | xp_cnn = Dropout(0.2)(xp_cnn) 25 | xp_cnn = Conv1D(filters=45, kernel_size=4, strides=1, activation='relu')(xp_cnn) 26 | xp_cnn = BatchNormalization()(xp_cnn) 27 | xp_cnn = Dropout(0.2)(xp_cnn) 28 | # xp_cnn = Bidirectional(LSTM(32,return_sequences=True))(xp_cnn) 29 | # xp_cnn = LSTM(32,return_sequences=True)(xp_cnn) 30 | xp_cnn = Flatten()(xp_cnn) 31 | xp_out_conjoint_cnn = Dense(64)(xp_cnn) 32 | xp_out_conjoint_cnn = Dropout(0.2)(xp_out_conjoint_cnn) 33 | 34 | # NN for RNA feature analysis by one hot encoding 35 | xr_in_conjoint_cnn = Input(shape=(rna_coding_length, vec_len_r)) 36 | xr_cnn = Conv1D(filters=45, kernel_size=6, strides=1, activation='relu')(xr_in_conjoint_cnn) 37 | xr_cnn = MaxPooling1D(pool_size=2)(xr_cnn) 38 | xr_cnn = BatchNormalization()(xr_cnn) 39 | xr_cnn = Dropout(0.2)(xr_cnn) 40 | xr_cnn = Conv1D(filters=64, kernel_size=5, strides=1, activation='relu')(xr_cnn) 41 | xr_cnn = MaxPooling1D(pool_size=2)(xr_cnn) 42 | xr_cnn = BatchNormalization()(xr_cnn) 43 | xr_cnn = Dropout(0.2)(xr_cnn) 44 | xr_cnn = Conv1D(filters=45, kernel_size=4, strides=1, activation='relu')(xr_cnn) 45 | xr_cnn = BatchNormalization()(xr_cnn) 46 | xr_cnn = Dropout(0.2)(xr_cnn) 47 | # xr_cnn = Bidirectional(LSTM(32,return_sequences=True))(xr_cnn) 48 | # xr_cnn = LSTM(32,return_sequences=True)(xr_cnn) 49 | xr_cnn = Flatten()(xr_cnn) 50 | xr_out_conjoint_cnn = Dense(64)(xr_cnn) 51 | xr_out_conjoint_cnn = Dropout(0.2)(xr_out_conjoint_cnn) 52 | 53 | x_out_conjoint_cnn = concatenate([xp_out_conjoint_cnn, xr_out_conjoint_cnn]) 54 | x_out_conjoint_cnn = Dense(128, kernel_initializer='random_uniform', activation='relu')(x_out_conjoint_cnn) 55 | # x_out_conjoint_cnn = Dropout(0.25)(x_out_conjoint_cnn) 56 | x_out_conjoint_cnn = BatchNormalization()(x_out_conjoint_cnn) 57 | # x_out_conjoint_cnn = Dropout(0.3)(x_out_conjoint_cnn) 58 | x_out_conjoint_cnn = Dense(64, kernel_initializer='random_uniform', activation='relu')(x_out_conjoint_cnn) 59 | y_conjoint_cnn = Dense(2, activation='softmax')(x_out_conjoint_cnn) 60 | 61 | model_conjoint_cnn = Model(inputs=[xp_in_conjoint_cnn, xr_in_conjoint_cnn], outputs=y_conjoint_cnn) 62 | 63 | 64 | 65 | return model_conjoint_cnn 66 | 67 | 68 | def conjoint_sae(encoders_protein, encoders_rna, pro_coding_length, rna_coding_length): 69 | 70 | # NN for protein feature analysis 71 | xp_in_conjoint = Input(shape=(pro_coding_length,)) 72 | xp_encoded = encoders_protein[0](xp_in_conjoint) 73 | xp_encoded = Dropout(0.2)(xp_encoded) 74 | xp_encoded = encoders_protein[1](xp_encoded) 75 | xp_encoded = Dropout(0.2)(xp_encoded) 76 | xp_encoder = encoders_protein[2](xp_encoded) 77 | xp_encoder = Dropout(0.2)(xp_encoder) 78 | xp_encoder = BatchNormalization()(xp_encoder) 79 | # xp_encoder = PReLU()(xp_encoder) 80 | xp_encoder = Dropout(0.2)(xp_encoder) 81 | 82 | # NN for RNA feature analysis 83 | xr_in_conjoint = Input(shape=(rna_coding_length,)) 84 | xr_encoded = encoders_rna[0](xr_in_conjoint) 85 | xr_encoded = Dropout(0.2)(xr_encoded) 86 | xr_encoded = encoders_rna[1](xr_encoded) 87 | xr_encoded = Dropout(0.2)(xr_encoded) 88 | xr_encoded = encoders_rna[2](xr_encoded) 89 | xr_encoder = Dropout(0.2)(xr_encoded) 90 | xr_encoder = BatchNormalization()(xr_encoder) 91 | # xr_encoder = PReLU()(xr_encoder) 92 | xr_encoder = Dropout(0.2)(xr_encoder) 93 | 94 | x_out_conjoint = concatenate([xp_encoder, xr_encoder]) 95 | x_out_conjoint = Dense(128, kernel_initializer='random_uniform', activation='relu')(x_out_conjoint) 96 | x_out_conjoint = BatchNormalization()(x_out_conjoint) 97 | x_out_conjoint = Dense(64, kernel_initializer='random_uniform', activation='relu')(x_out_conjoint) 98 | y_conjoint = Dense(2, activation='softmax')(x_out_conjoint) 99 | 100 | model_conjoint = Model(inputs=[xp_in_conjoint, xr_in_conjoint], outputs=y_conjoint) 101 | 102 | 103 | return model_conjoint -------------------------------------------------------------------------------- /data/RPI369_pos_pairs.txt: -------------------------------------------------------------------------------- 1 | 1drz-A 1drz-B 1 2 | 2r93-B 2r93-R 1 3 | 2r93-A 2r93-R 1 4 | 1vfg-A 1vfg-C 1 5 | 1jbt-A 1jbt-C 1 6 | 1ec6-B 1ec6-C 1 7 | 1f8v-C 1f8v-R 1 8 | 1f8v-E 1f8v-R 1 9 | 3cw1-G 3cw1-V 1 10 | 3cw1-B 3cw1-V 1 11 | 3cw1-T 3cw1-w 1 12 | 3cw1-P 3cw1-v 1 13 | 3cw1-Y 3cw1-x 1 14 | 3cw1-2 3cw1-x 1 15 | 3cw1-5 3cw1-x 1 16 | 3cw1-K 3cw1-V 1 17 | 3cw1-J 3cw1-x 1 18 | 3cw1-9 3cw1-v 1 19 | 1hq1-A 1hq1-B 1 20 | 1dul-A 1dul-B 1 21 | 2nqp-C 2nqp-F 1 22 | 2az2-A 2az2-C 1 23 | 2hgh-A 2hgh-B 1 24 | 2r92-B 2r92-T 1 25 | 2r92-A 2r92-T 1 26 | 2b2d-C 2b2d-S 1 27 | 2izn-A 2izn-R 1 28 | 1r3e-A 1r3e-E 1 29 | 3ks8-A 3ks8-F 1 30 | 2b2e-C 2b2e-R 1 31 | 2xfz-Y 2xfz-X 1 32 | 2wri-Y 2wri-X 1 33 | 1ze2-B 1ze2-D 1 34 | 1ze2-A 1ze2-C 1 35 | 3mum-P 3mum-R 1 36 | 3mur-P 3mur-R 1 37 | 1x1l-X 1x1l-A 1 38 | 1rpu-B 1rpu-D 1 39 | 2zh4-A 2zh4-B 1 40 | 2ply-A 2ply-C 1 41 | 1wsu-A 1wsu-G 1 42 | 1urn-C 1urn-R 1 43 | 2zh3-A 2zh3-B 1 44 | 2jpp-B 2jpp-D 1 45 | 2wrn-Z 2wrn-Y 1 46 | 3cz3-A 3cz3-E 1 47 | 2cv1-B 2cv1-D 1 48 | 3cz3-B 3cz3-G 1 49 | 1aq4-C 1aq4-S 1 50 | 2c4y-C 2c4y-S 1 51 | 1fje-B 1fje-A 1 52 | 3kfu-H 3kfu-N 1 53 | 3kfu-B 3kfu-N 1 54 | 3kfu-F 3kfu-L 1 55 | 3kfu-G 3kfu-L 1 56 | 1qzw-A 1qzw-B 1 57 | 2dlc-X 2dlc-Y 1 58 | 1zdh-A 1zdh-R 1 59 | 1p6v-C 1p6v-B 1 60 | 2gje-D 2gje-R 1 61 | 2gje-A 2gje-R 1 62 | 2iz8-A 2iz8-R 1 63 | 2wyy-J 2wyy-S 1 64 | 2fk6-A 2fk6-R 1 65 | 1q2r-C 1q2r-F 1 66 | 3deg-C 3deg-G 1 67 | 1a9n-D 1a9n-R 1 68 | 1a9n-A 1a9n-Q 1 69 | 2iy3-A 2iy3-B 1 70 | 2pxv-A 2pxv-B 1 71 | 1urn-A 1urn-P 1 72 | 3htx-D 3htx-E 1 73 | 3ciy-A 3ciy-D 1 74 | 3ktv-B 3ktv-A 1 75 | 2d6f-D 2d6f-F 1 76 | 3hjy-A 3hjy-C 1 77 | 2c50-A 2c50-R 1 78 | 1h3e-A 1h3e-B 1 79 | 1f6u-A 1f6u-B 1 80 | 2j0q-I 2j0q-H 1 81 | 2j0q-B 2j0q-H 1 82 | 1aud-A 1aud-B 1 83 | 1jid-A 1jid-B 1 84 | 1kog-D 1kog-K 1 85 | 1zbh-D 1zbh-F 1 86 | 1q2s-A 1q2s-E 1 87 | 2nuf-A 2nuf-C 1 88 | 2b3j-C 2b3j-G 1 89 | 2xlj-A 2xlj-B 1 90 | 2b2d-C 2b2d-R 1 91 | 2der-B 2der-D 1 92 | 3iab-A 3iab-R 1 93 | 3iab-B 3iab-R 1 94 | 2xli-A 2xli-B 1 95 | 1zdi-A 1zdi-R 1 96 | 3epl-B 3epl-E 1 97 | 2jea-B 2jea-C 1 98 | 2jea-A 2jea-C 1 99 | 1u1y-B 1u1y-S 1 100 | 2csx-A 2csx-D 1 101 | 1ry1-D 1ry1-M 1 102 | 1ry1-C 1ry1-M 1 103 | 5msf-C 5msf-S 1 104 | 3foz-B 3foz-C 1 105 | 1jbr-A 1jbr-C 1 106 | 2fy1-A 2fy1-B 1 107 | 2c4z-A 2c4z-R 1 108 | 1qf6-A 1qf6-B 1 109 | 2ake-A 2ake-B 1 110 | 3ftf-A 3ftf-D 1 111 | 2b9m-Y 2b9m-X 1 112 | 2xlk-B 2xlk-D 1 113 | 2iy5-B 2iy5-T 1 114 | 1eiy-A 1eiy-C 1 115 | 3hl2-B 3hl2-E 1 116 | 1t4l-B 1t4l-A 1 117 | 3cz3-D 3cz3-F 1 118 | 2ob7-B 2ob7-A 1 119 | 1aq3-C 1aq3-R 1 120 | 2izm-C 2izm-S 1 121 | 2c51-C 2c51-S 1 122 | 1j2b-B 1j2b-C 1 123 | 2zh9-A 2zh9-B 1 124 | 3boy-C 3boy-D 1 125 | 5msf-A 5msf-R 1 126 | 2b3j-C 2b3j-H 1 127 | 2az2-B 2az2-D 1 128 | 3iwn-D 3iwn-B 1 129 | 2dra-A 2dra-B 1 130 | 2f8k-A 2f8k-B 1 131 | 1k8w-A 1k8w-B 1 132 | 2izm-C 2izm-R 1 133 | 1u6p-A 1u6p-B 1 134 | 1exd-A 1exd-B 1 135 | 2gjw-C 2gjw-I 1 136 | 3dkn-A 3dkn-F 1 137 | 3akz-B 3akz-E 1 138 | 3al0-B 3al0-E 1 139 | 3al0-C 3al0-E 1 140 | 2bu1-C 2bu1-S 1 141 | 2bs0-C 2bs0-S 1 142 | 2j0s-T 2j0s-E 1 143 | 2j0s-A 2j0s-E 1 144 | 1xok-C 1xok-A 1 145 | 1l1c-A 1l1c-C 1 146 | 2bq5-A 2bq5-R 1 147 | 2pxf-A 2pxf-B 1 148 | 3a2k-B 3a2k-D 1 149 | 2gjw-B 2gjw-E 1 150 | 2ese-A 2ese-B 1 151 | 1aq3-C 1aq3-S 1 152 | 3ivk-B 3ivk-C 1 153 | 2dr5-A 2dr5-B 1 154 | 2oj3-A 2oj3-B 1 155 | 2b63-B 2b63-R 1 156 | 2b63-A 2b63-R 1 157 | 1aq4-A 1aq4-R 1 158 | 1ekz-A 1ekz-B 1 159 | 1zdi-B 1zdi-S 1 160 | 2w2h-A 2w2h-S 1 161 | 2w2h-D 2w2h-S 1 162 | 2jpp-B 2jpp-C 1 163 | 1j1u-A 1j1u-B 1 164 | 1b23-P 1b23-R 1 165 | 1u0b-B 1u0b-A 1 166 | 2nue-B 2nue-C 1 167 | 2nr0-C 2nr0-H 1 168 | 2ez6-A 2ez6-C 1 169 | 1zbh-A 1zbh-E 1 170 | 3d5c-X 3d5c-V 1 171 | 2b6g-A 2b6g-B 1 172 | 3eq4-X 3eq4-Y 1 173 | 2az0-B 2az0-D 1 174 | 1il2-A 1il2-D 1 175 | 1asz-B 1asz-R 1 176 | 1r3e-A 1r3e-C 1 177 | 2b64-Y 2b64-X 1 178 | 1q2s-B 1q2s-F 1 179 | 2pxu-A 2pxu-B 1 180 | 3deg-C 3deg-I 1 181 | 2zh1-A 2zh1-B 1 182 | 2c4z-C 2c4z-S 1 183 | 3a6p-H 3a6p-J 1 184 | 3a6p-F 3a6p-J 1 185 | 2b3j-A 2b3j-F 1 186 | 2b2g-C 2b2g-S 1 187 | 2c4q-C 2c4q-S 1 188 | 2ab4-A 2ab4-B 1 189 | 3dkn-A 3dkn-D 1 190 | 3eq3-X 3eq3-Y 1 191 | 2rfk-A 2rfk-E 1 192 | 2wyy-F 2wyy-R 1 193 | 2bny-B 2bny-R 1 194 | 1m5k-C 1m5k-B 1 195 | 2j28-9 2j28-8 1 196 | 1e8s-B 1e8s-C 1 197 | 1e8s-A 1e8s-C 1 198 | 3k0j-D 3k0j-F 1 199 | 2ozb-D 2ozb-F 1 200 | 2ozb-B 2ozb-C 1 201 | 1r9f-A 1r9f-B 1 202 | 2xb2-T 2xb2-E 1 203 | 2xb2-A 2xb2-E 1 204 | 2drb-A 2drb-B 1 205 | 1ob5-C 1ob5-B 1 206 | 2b2g-C 2b2g-R 1 207 | 2c50-C 2c50-S 1 208 | 2zhb-A 2zhb-B 1 209 | 1urn-B 1urn-Q 1 210 | 3ktw-A 3ktw-C 1 211 | 2du4-A 2du4-C 1 212 | 2dr2-A 2dr2-B 1 213 | 2azx-A 2azx-C 1 214 | 2pxe-A 2pxe-B 1 215 | 2bte-D 2bte-E 1 216 | 2x7n-B 2x7n-A 1 217 | 1u1y-A 1u1y-R 1 218 | 3cz3-D 3cz3-H 1 219 | 2bs1-B 2bs1-R 1 220 | 1rpu-A 1rpu-C 1 221 | 3mr8-V 3mr8-W 1 222 | 3kix-y 3kix-v 1 223 | 3huy-V 3huy-W 1 224 | 2fmt-B 2fmt-C 1 225 | 2xfz-Y 2xfz-V 1 226 | 2wrk-Y 2wrk-V 1 227 | 1sjf-A 1sjf-B 1 228 | 1vbx-A 1vbx-B 1 229 | 1vby-A 1vby-B 1 230 | 2az0-A 2az0-C 1 231 | 2bny-C 2bny-S 1 232 | 2v0g-A 2v0g-F 1 233 | 3id5-A 3id5-D 1 234 | 2anr-A 2anr-B 1 235 | 2pjp-A 2pjp-B 1 236 | 1ser-B 1ser-T 1 237 | 2pxl-A 2pxl-B 1 238 | 1h4s-A 1h4s-T 1 239 | 1hvu-K 1hvu-L 1 240 | 1hvu-A 1hvu-F 1 241 | 1hvu-D 1hvu-C 1 242 | 1rkj-A 1rkj-B 1 243 | 1dz5-A 1dz5-C 1 244 | 2gtt-T 2gtt-X 1 245 | 2bu1-C 2bu1-R 1 246 | 2gtt-D 2gtt-W 1 247 | 2iz9-C 2iz9-R 1 248 | 2pxp-A 2pxp-B 1 249 | 2zzn-A 2zzn-D 1 250 | 1qu3-A 1qu3-T 1 251 | 1r9f-A 1r9f-C 1 252 | 2xlk-A 2xlk-C 1 253 | 1qfq-B 1qfq-A 1 254 | 1efw-B 1efw-D 1 255 | 1c0a-A 1c0a-B 1 256 | 2hvy-A 2hvy-E 1 257 | 2bs0-C 2bs0-R 1 258 | 3hax-A 3hax-E 1 259 | 2f8s-A 2f8s-C 1 260 | 2go5-W 2go5-A 1 261 | 2go5-B 2go5-A 1 262 | 2go5-2 2go5-A 1 263 | 2go5-1 2go5-A 1 264 | 2bx2-L 2bx2-R 1 265 | 1gtn-L 1gtn-W 1 266 | 1gtn-C 1gtn-W 1 267 | 1a1t-A 1a1t-B 1 268 | 1f7u-A 1f7u-B 1 269 | 2bs1-C 2bs1-S 1 270 | 3a6p-C 3a6p-D 1 271 | 3a6p-F 3a6p-I 1 272 | 1gax-A 1gax-C 1 273 | 1zjw-A 1zjw-B 1 274 | 3kiu-y 3kiu-a 1 275 | 3hjy-A 3hjy-D 1 276 | 1r3e-A 1r3e-D 1 277 | 3hhn-D 3hhn-E 1 278 | 3htx-A 3htx-C 1 279 | 2pxt-A 2pxt-B 1 280 | 2i91-B 2i91-F 1 281 | 2zh7-A 2zh7-B 1 282 | 1ec6-A 1ec6-D 1 283 | 1wz2-B 1wz2-D 1 284 | 2zzm-A 2zzm-B 1 285 | 2c51-C 2c51-R 1 286 | 3g8s-D 3g8s-R 1 287 | 2b3j-B 2b3j-E 1 288 | 1lng-A 1lng-B 1 289 | 3dd2-H 3dd2-B 1 290 | 3dd2-L 3dd2-B 1 291 | 2r8s-L 2r8s-R 1 292 | 2pxq-A 2pxq-B 1 293 | 3kis-y 3kis-x 1 294 | 1zdk-B 1zdk-S 1 295 | 2xb2-S 2xb2-R 1 296 | 2xb2-X 2xb2-R 1 297 | 1q2r-D 1q2r-E 1 298 | 2iz8-C 2iz8-S 1 299 | 1ry1-D 1ry1-E 1 300 | 1ry1-C 1ry1-E 1 301 | 2zh2-A 2zh2-B 1 302 | 2ct8-B 2ct8-D 1 303 | 2zh6-A 2zh6-B 1 304 | 1ry1-D 1ry1-N 1 305 | 1ry1-C 1ry1-N 1 306 | 2c4q-B 2c4q-R 1 307 | 1gtf-L 1gtf-W 1 308 | 2b2e-C 2b2e-S 1 309 | 2r92-B 2r92-P 1 310 | 2r92-A 2r92-P 1 311 | 1e7k-B 1e7k-C 1 312 | 1zdk-A 1zdk-R 1 313 | 1mfq-C 1mfq-A 1 314 | 1ry1-W 1ry1-A 1 315 | 1ry1-B 1ry1-A 1 316 | 1l9a-A 1l9a-B 1 317 | 2j37-W 2j37-A 1 318 | 2i91-A 2i91-D 1 319 | 2czj-G 2czj-H 1 320 | 3deg-C 3deg-J 1 321 | 2zh8-A 2zh8-B 1 322 | 3bo3-A 3bo3-B 1 323 | 3hhz-N 3hhz-R 1 324 | 1zse-A 1zse-R 1 325 | 3dkn-A 3dkn-E 1 326 | 2zni-A 2zni-C 1 327 | 2bq5-C 2bq5-S 1 328 | 3egz-A 3egz-B 1 329 | 2zko-A 2zko-C 1 330 | 2zi0-B 2zi0-D 1 331 | 1ooa-A 1ooa-C 1 332 | 1jbr-B 1jbr-D 1 333 | 3foz-B 3foz-D 1 334 | 2iz9-C 2iz9-S 1 335 | 3cun-A 3cun-C 1 336 | 3cul-A 3cul-C 1 337 | 2j0q-T 2j0q-E 1 338 | 2j0q-A 2j0q-E 1 339 | 3ks8-A 3ks8-E 1 340 | 2dr7-A 2dr7-B 1 341 | 2gic-E 2gic-R 1 342 | 2ipy-A 2ipy-D 1 343 | 3icq-C 3icq-E 1 344 | 3icq-U 3icq-E 1 345 | 2rkj-N 2rkj-O 1 346 | 3moj-B 3moj-A 1 347 | 1hji-B 1hji-A 1 348 | 1qrt-A 1qrt-B 1 349 | 1c9s-F 1c9s-W 1 350 | 2v3c-D 2v3c-M 1 351 | 2v3c-A 2v3c-M 1 352 | 2wh1-Y 2wh1-V 1 353 | 2zxu-A 2zxu-C 1 354 | 2ann-A 2ann-B 1 355 | 1zdh-B 1zdh-S 1 356 | 3hay-A 3hay-E 1 357 | 2zuf-A 2zuf-B 1 358 | 2izn-C 2izn-S 1 359 | 3hjw-A 3hjw-D 1 360 | 2c4y-A 2c4y-R 1 361 | 1ttt-B 1ttt-D 1 362 | 1ml5-Z 1ml5-B 1 363 | 3deg-C 3deg-A 1 364 | 1ttt-A 1ttt-D 1 365 | 1sz1-B 1sz1-F 1 366 | 1zo1-I 1zo1-F 1 367 | 3adc-A 3adc-C 1 368 | 2f8t-A 2f8t-D 1 369 | 2ihx-A 2ihx-B 1 -------------------------------------------------------------------------------- /sample/utils/callbacks.py: -------------------------------------------------------------------------------- 1 | import matplotlib 2 | matplotlib.use("Agg") 3 | 4 | import warnings 5 | import numpy as np 6 | import matplotlib.pyplot as plt 7 | from keras.callbacks import Callback 8 | 9 | 10 | # to consider version compatibility 11 | def get_monitor_value(logs, monitor): 12 | if (monitor == 'acc') and ('acc' not in logs.keys()): 13 | monitor = 'accuracy' 14 | monitor_value = logs.get(monitor) 15 | if monitor_value is None: 16 | warnings.warn( 17 | 'Early stopping conditioned on metric `%s` ' 18 | 'which is not available. Available metrics are: %s' % 19 | (monitor, ','.join(list(logs.keys()))), RuntimeWarning 20 | ) 21 | return monitor_value 22 | 23 | 24 | class AccHistoryPlot(Callback): 25 | def __init__(self, stage_infos, test_data, data_name, result_save_path, validate=0, plot_epoch_gap=30, verbose=1): 26 | super(AccHistoryPlot, self).__init__() 27 | self.stage, self.fold = stage_infos 28 | self.X_test, self.y_test = test_data 29 | self.data_name = data_name 30 | self.result_save_path = result_save_path 31 | self.plot_epoch_gap = plot_epoch_gap 32 | self.validate = validate 33 | self.verbose = verbose 34 | 35 | self.close_plt_on_train_end = True 36 | 37 | fig = plt.figure() 38 | self.ax = fig.add_subplot(1, 1, 1) 39 | plt.xlabel('Training epochs', fontsize=13) 40 | plt.ylabel('Accuracy and loss values', fontsize=13) 41 | plt.title('$Acc$ and loss of {} on {}'.format(self.stage, self.data_name), fontsize=15) 42 | plt.ion() 43 | 44 | self.train_accs = [] 45 | self.train_loss = [] 46 | self.val_accs = [] 47 | self.val_loss = [] 48 | self.test_accs = [] 49 | self.test_loss = [] 50 | 51 | self.line_train_acc = None 52 | self.line_val_acc = None 53 | self.line_test_acc = None 54 | self.line_train_loss = None 55 | self.line_val_loss = None 56 | self.line_test_loss = None 57 | 58 | def plot(self): 59 | if self.line_train_acc: 60 | self.ax.lines.remove(self.line_train_acc[0]) 61 | self.ax.lines.remove(self.line_test_acc[0]) 62 | self.ax.lines.remove(self.line_train_loss[0]) 63 | self.ax.lines.remove(self.line_test_loss[0]) 64 | if self.validate: 65 | self.ax.lines.remove(self.line_val_acc[0]) 66 | self.ax.lines.remove(self.line_val_acc[0]) 67 | self.line_train_acc = self.ax.plot(self.train_accs, lw=1.8, color='deepskyblue', label='Train $Acc$') 68 | if self.validate: 69 | self.line_val_acc = self.ax.plot(self.val_accs, lw=1.8, color='gold', label='Val $Acc$') 70 | self.line_test_acc = self.ax.plot(self.test_accs, lw=1.8, color='limegreen', label = 'Test $Acc$') 71 | self.line_train_loss = self.ax.plot(self.train_loss, lw=1.8, color='coral', label='Train Loss') 72 | if self.validate: 73 | self.line_val_acc = self.ax.plot(self.val_accs, lw=1.8, color='darkred', label='Val Loss') 74 | self.line_test_loss = self.ax.plot(self.test_loss, lw=1.8, color='darkorange', label = 'Test Loss') 75 | 76 | 77 | self.ax.legend(loc='center right' if not self.validate else 'best', fontsize=10) 78 | plt.pause(0.1) 79 | 80 | def on_epoch_end(self, epoch, logs=None): 81 | test_loss, test_acc = self.model.evaluate(x=self.X_test, y=self.y_test, batch_size=200, verbose=0) 82 | if self.verbose: 83 | print("Current loss: {}, acc: {}".format(test_loss, test_acc)) 84 | self.test_accs.append(test_acc) 85 | self.train_accs.append(get_monitor_value(logs, 'acc')) 86 | self.test_loss.append(test_loss) 87 | self.train_loss.append(get_monitor_value(logs, 'loss')) 88 | if self.validate: 89 | self.val_accs.append(get_monitor_value(logs, 'val_acc')) 90 | self.val_loss.append(get_monitor_value(logs, 'val_loss')) 91 | if epoch % self.plot_epoch_gap == 0: 92 | self.plot() 93 | 94 | def on_train_end(self, logs=None): 95 | self.plot() 96 | 97 | if self.fold!=None: 98 | plot_file_name = 'fold {} of {} stage.pdf'.format(self.fold, self.stage) 99 | else: 100 | plot_file_name = '{} stage.pdf'.format(self.stage) 101 | 102 | plt.savefig(self.result_save_path + plot_file_name) 103 | if self.close_plt_on_train_end: 104 | plt.close() 105 | 106 | 107 | class EarlyStopping(Callback): 108 | """Stop training when a monitored quantity has stopped improving. 109 | # Arguments 110 | monitor: quantity to be monitored. 111 | min_delta: minimum change in the monitored quantity 112 | to qualify as an improvement, i.e. an absolute 113 | change of less than min_delta, will count as no 114 | improvement. 115 | patience: number of epochs that produced the monitored 116 | quantity with no improvement after which training will 117 | be stopped. 118 | Validation quantities may not be produced for every 119 | epoch, if the validation frequency 120 | (`model.fit(validation_freq=5)`) is greater than one. 121 | verbose: verbosity mode. 122 | mode: one of {auto, min, max}. In `min` mode, 123 | training will stop when the quantity 124 | monitored has stopped decreasing; in `max` 125 | mode it will stop when the quantity 126 | monitored has stopped increasing; in `auto` 127 | mode, the direction is automatically inferred 128 | from the name of the monitored quantity. 129 | baseline: Baseline value for the monitored quantity to reach. 130 | Training will stop if the model doesn't show improvement 131 | over the baseline. 132 | restore_best_weights: whether to restore model weights from 133 | the epoch with the best value of the monitored quantity. 134 | If False, the model weights obtained at the last step of 135 | training are used. 136 | """ 137 | 138 | def __init__(self, 139 | monitor='val_loss', 140 | min_delta=0, 141 | patience=0, 142 | verbose=0, 143 | mode='auto', 144 | baseline=None, 145 | restore_best_weights=False): 146 | super(EarlyStopping, self).__init__() 147 | 148 | self.monitor = monitor 149 | self.baseline = baseline 150 | self.patience = patience 151 | self.verbose = verbose 152 | self.min_delta = min_delta 153 | self.wait = 0 154 | self.stopped_epoch = 0 155 | self.restore_best_weights = restore_best_weights 156 | self.best_weights = None 157 | 158 | if mode not in ['auto', 'min', 'max']: 159 | warnings.warn('EarlyStopping mode %s is unknown, ' 160 | 'fallback to auto mode.' % mode, 161 | RuntimeWarning) 162 | mode = 'auto' 163 | 164 | if mode == 'min': 165 | self.monitor_op = np.less 166 | elif mode == 'max': 167 | self.monitor_op = np.greater 168 | else: 169 | if 'acc' in self.monitor: 170 | self.monitor_op = np.greater 171 | else: 172 | self.monitor_op = np.less 173 | 174 | if self.monitor_op == np.greater: 175 | self.min_delta *= 1 176 | else: 177 | self.min_delta *= -1 178 | 179 | def on_train_begin(self, logs=None): 180 | # Allow instances to be re-used 181 | self.wait = 0 182 | self.stopped_epoch = 0 183 | if self.baseline is not None: 184 | self.best = self.baseline 185 | else: 186 | self.best = np.Inf if self.monitor_op == np.less else -np.Inf 187 | 188 | def on_epoch_end(self, epoch, logs=None): 189 | current = get_monitor_value(logs, self.monitor) 190 | if current is None: 191 | return 192 | 193 | if self.monitor_op(current - self.min_delta, self.best): 194 | self.best = current 195 | self.wait = 0 196 | if self.restore_best_weights: 197 | self.best_weights = self.model.get_weights() 198 | else: 199 | self.wait += 1 200 | if self.wait >= self.patience: 201 | self.stopped_epoch = epoch 202 | self.model.stop_training = True 203 | if self.restore_best_weights: 204 | if self.verbose > 0: 205 | print('Restoring model weights from the end of ' 206 | 'the best epoch') 207 | self.model.set_weights(self.best_weights) 208 | 209 | def on_train_end(self, logs=None): 210 | if self.stopped_epoch > 0 and self.verbose > 0: 211 | print('Epoch %05d: early stopping' % (self.stopped_epoch + 1)) 212 | -------------------------------------------------------------------------------- /data/RPI488_pairs.txt: -------------------------------------------------------------------------------- 1 | 1FFK_A 1FFK_0 1 2 | 1FFK_C 1FFK_0 1 3 | 1FFK_D 1FFK_0 1 4 | 1FFK_D 1FFK_9 1 5 | 1FFK_E 1FFK_0 1 6 | 1FFK_F 1FFK_0 1 7 | 1FFK_G 1FFK_0 1 8 | 1FFK_H 1FFK_0 1 9 | 1FFK_I 1FFK_0 1 10 | 1FFK_J 1FFK_0 1 11 | 1FFK_K 1FFK_0 1 12 | 1FFK_K 1FFK_9 1 13 | 1FFK_L 1FFK_0 1 14 | 1FFK_M 1FFK_0 1 15 | 1FFK_N 1FFK_0 1 16 | 1FFK_N 1FFK_9 1 17 | 1FFK_O 1FFK_0 1 18 | 1FFK_P 1FFK_0 1 19 | 1FFK_Q 1FFK_0 1 20 | 1FFK_R 1FFK_0 1 21 | 1FFK_S 1FFK_0 1 22 | 1FFK_T 1FFK_0 1 23 | 1FFK_T 1FFK_9 1 24 | 1FFK_U 1FFK_0 1 25 | 1FFK_W 1FFK_0 1 26 | 1FFK_X 1FFK_0 1 27 | 1FFK_Y 1FFK_0 1 28 | 1FFK_Z 1FFK_0 1 29 | 1FFK_1 1FFK_0 1 30 | 1GIY_C 1GIY_A 1 31 | 1GIY_D 1GIY_A 1 32 | 1GIY_E 1GIY_A 1 33 | 1GIY_H 1GIY_A 1 34 | 1GIY_I 1GIY_A 1 35 | 1GIY_K 1GIY_A 1 36 | 1GIY_L 1GIY_A 1 37 | 1GIY_N 1GIY_A 1 38 | 1GIY_S 1GIY_A 1 39 | 1GIY_V 1GIY_B 1 40 | 1GIY_X 1GIY_A 1 41 | 1J5A_A 1J5A_K 1 42 | 1J5A_A 1J5A_K 1 43 | 1J5A_A 1J5A_K 1 44 | 1P85_A 1P85_0 1 45 | 1P85_B 1P85_0 1 46 | 1P85_D 1P85_0 1 47 | 1P85_E 1P85_0 1 48 | 1P85_H 1P85_0 1 49 | 1P85_I 1P85_0 1 50 | 1P85_K 1P85_0 1 51 | 1P85_N 1P85_0 1 52 | 1P85_R 1P85_0 1 53 | 1P85_S 1P85_0 1 54 | 1P85_W 1P85_0 1 55 | 1P85_X 1P85_0 1 56 | 1P85_1 1P85_0 1 57 | 1P85_4 1P85_0 1 58 | 2FTC_A 2FTC_R 1 59 | 2FTC_B 2FTC_R 1 60 | 2FTC_C 2FTC_R 1 61 | 2FTC_D 2FTC_R 1 62 | 2FTC_F 2FTC_R 1 63 | 2FTC_G 2FTC_R 1 64 | 2FTC_H 2FTC_R 1 65 | 2FTC_I 2FTC_R 1 66 | 2FTC_J 2FTC_R 1 67 | 2FTC_L 2FTC_R 1 68 | 2FTC_M 2FTC_R 1 69 | 2FTC_N 2FTC_R 1 70 | 2FTC_O 2FTC_R 1 71 | 2FTC_P 2FTC_R 1 72 | 2FTC_Q 2FTC_R 1 73 | 2GYA_2 2GYA_0 1 74 | 2GYA_3 2GYA_0 1 75 | 2GYA_F 2GYA_0 1 76 | 2GYA_U 2GYA_0 1 77 | 2R8S_H 2R8S_R 1 78 | 2RKJ_A 2RKJ_C 1 79 | 2ZJP_D 2ZJP_Z 1 80 | 2ZJP_J 2ZJP_Z 1 81 | 2ZJP_L 2ZJP_Z 1 82 | 2ZJP_S 2ZJP_Z 1 83 | 2ZKQ_a 2ZKQ_A 1 84 | 2ZKQ_b 2ZKQ_A 1 85 | 2ZKQ_c 2ZKQ_A 1 86 | 2ZKQ_d 2ZKQ_A 1 87 | 2ZKQ_e 2ZKQ_A 1 88 | 2ZKQ_g 2ZKQ_A 1 89 | 2ZKQ_h 2ZKQ_A 1 90 | 2ZKQ_i 2ZKQ_A 1 91 | 2ZKQ_j 2ZKQ_A 1 92 | 2ZKQ_k 2ZKQ_A 1 93 | 2ZKQ_l 2ZKQ_A 1 94 | 2ZKQ_m 2ZKQ_A 1 95 | 2ZKQ_n 2ZKQ_A 1 96 | 2ZKQ_o 2ZKQ_A 1 97 | 2ZKQ_q 2ZKQ_A 1 98 | 2ZKQ_s 2ZKQ_A 1 99 | 2ZKR_a 2ZKR_0 1 100 | 2ZKR_b 2ZKR_0 1 101 | 2ZKR_c 2ZKR_0 1 102 | 2ZKR_c 2ZKR_1 1 103 | 2ZKR_d 2ZKR_0 1 104 | 2ZKR_d 2ZKR_Y 1 105 | 2ZKR_e 2ZKR_0 1 106 | 2ZKR_f 2ZKR_0 1 107 | 2ZKR_g 2ZKR_0 1 108 | 2ZKR_h 2ZKR_0 1 109 | 2ZKR_i 2ZKR_0 1 110 | 2ZKR_j 2ZKR_0 1 111 | 2ZKR_k 2ZKR_0 1 112 | 2ZKR_l 2ZKR_0 1 113 | 2ZKR_l 2ZKR_F 1 114 | 2ZKR_l 2ZKR_X 1 115 | 2ZKR_m 2ZKR_0 1 116 | 2ZKR_m 2ZKR_X 1 117 | 2ZKR_n 2ZKR_0 1 118 | 2ZKR_n 2ZKR_Y 1 119 | 2ZKR_o 2ZKR_0 1 120 | 2ZKR_o 2ZKR_X 1 121 | 2ZKR_p 2ZKR_0 1 122 | 2ZKR_p 2ZKR_X 1 123 | 2ZKR_q 2ZKR_0 1 124 | 2ZKR_q 2ZKR_Y 1 125 | 2ZKR_r 2ZKR_1 1 126 | 2ZKR_r 2ZKR_0 1 127 | 2ZKR_s 2ZKR_0 1 128 | 2ZKR_t 2ZKR_1 1 129 | 2ZKR_t 2ZKR_0 1 130 | 2ZKR_t 2ZKR_X 1 131 | 2ZKR_u 2ZKR_0 1 132 | 2ZKR_v 2ZKR_1 1 133 | 2ZKR_w 2ZKR_0 1 134 | 2ZKR_w 2ZKR_Y 1 135 | 2ZKR_x 2ZKR_0 1 136 | 2ZKR_y 2ZKR_0 1 137 | 2ZKR_z 2ZKR_0 1 138 | 2ZKR_2 2ZKR_0 1 139 | 2ZKR_2 2ZKR_1 1 140 | 2ZKR_3 2ZKR_0 1 141 | 2ZKR_3 2ZKR_1 1 142 | 2ZKR_4 2ZKR_0 1 143 | 2ZKR_5 2ZKR_0 1 144 | 2ZKR_6 2ZKR_0 1 145 | 3BBN_B 3BBN_A 1 146 | 3BBN_C 3BBN_A 1 147 | 3BBN_D 3BBN_A 1 148 | 3BBN_E 3BBN_A 1 149 | 3BBN_F 3BBN_A 1 150 | 3BBN_G 3BBN_A 1 151 | 3BBN_H 3BBN_A 1 152 | 3BBN_I 3BBN_A 1 153 | 3BBN_J 3BBN_A 1 154 | 3BBN_K 3BBN_A 1 155 | 3BBN_L 3BBN_A 1 156 | 3BBN_M 3BBN_A 1 157 | 3BBN_N 3BBN_A 1 158 | 3BBN_O 3BBN_A 1 159 | 3BBN_P 3BBN_A 1 160 | 3BBN_Q 3BBN_A 1 161 | 3BBN_R 3BBN_A 1 162 | 3BBN_S 3BBN_A 1 163 | 3BBN_T 3BBN_A 1 164 | 3BBN_U 3BBN_A 1 165 | 3BBO_D 3BBO_A 1 166 | 3BBO_E 3BBO_A 1 167 | 3BBO_F 3BBO_A 1 168 | 3BBO_F 3BBO_C 1 169 | 3BBO_G 3BBO_A 1 170 | 3BBO_H 3BBO_A 1 171 | 3BBO_H 3BBO_B 1 172 | 3BBO_I 3BBO_A 1 173 | 3BBO_J 3BBO_A 1 174 | 3BBO_K 3BBO_A 1 175 | 3BBO_L 3BBO_A 1 176 | 3BBO_M 3BBO_A 1 177 | 3BBO_N 3BBO_A 1 178 | 3BBO_O 3BBO_A 1 179 | 3BBO_O 3BBO_B 1 180 | 3BBO_P 3BBO_A 1 181 | 3BBO_P 3BBO_C 1 182 | 3BBO_Q 3BBO_A 1 183 | 3BBO_Q 3BBO_B 1 184 | 3BBO_R 3BBO_A 1 185 | 3BBO_R 3BBO_C 1 186 | 3BBO_S 3BBO_A 1 187 | 3BBO_T 3BBO_A 1 188 | 3BBO_U 3BBO_A 1 189 | 3BBO_V 3BBO_A 1 190 | 3BBO_W 3BBO_A 1 191 | 3BBO_X 3BBO_A 1 192 | 3BBO_Y 3BBO_A 1 193 | 3BBO_Z 3BBO_A 1 194 | 3BBO_2 3BBO_A 1 195 | 3BBO_2 3BBO_C 1 196 | 3BBO_3 3BBO_A 1 197 | 3BBO_4 3BBO_A 1 198 | 3BBO_5 3BBO_A 1 199 | 3BBO_6 3BBO_A 1 200 | 3CW1_A 3CW1_V 1 201 | 3CW1_B 3CW1_V 1 202 | 3CW1_L 3CW1_V 1 203 | 3HUW_V 3HUW_A 1 204 | 3JYV_B 3JYV_A 1 205 | 3JYV_C 3JYV_A 1 206 | 3JYV_D 3JYV_A 1 207 | 3JYV_E 3JYV_A 1 208 | 3JYV_G 3JYV_A 1 209 | 3JYV_H 3JYV_A 1 210 | 3JYV_I 3JYV_A 1 211 | 3JYV_J 3JYV_A 1 212 | 3JYV_K 3JYV_A 1 213 | 3JYV_L 3JYV_A 1 214 | 3JYV_M 3JYV_A 1 215 | 3JYV_N 3JYV_A 1 216 | 3JYV_O 3JYV_A 1 217 | 3JYV_Q 3JYV_A 1 218 | 3JYV_R 3JYV_A 1 219 | 3JYV_S 3JYV_A 1 220 | 3JYV_T 3JYV_A 1 221 | 2R8S_L 2R8S_R 1 222 | 2ZJP_T 2ZJP_Z 1 223 | 2ZJP_W 2ZJP_Z 1 224 | 2ZKR_m 2ZKR_1 1 225 | 3BBO_L 3BBO_C 1 226 | 3BBO_X 3BBO_B 1 227 | 3HUW_B 3HUW_A 1 228 | 3HUW_C 3HUW_A 1 229 | 3HUW_E 3HUW_A 1 230 | 3HUW_F 3HUW_A 1 231 | 3HUW_G 3HUW_A 1 232 | 3HUW_H 3HUW_A 1 233 | 3HUW_I 3HUW_A 1 234 | 3HUW_J 3HUW_A 1 235 | 3HUW_K 3HUW_A 1 236 | 3HUW_L 3HUW_A 1 237 | 3HUW_M 3HUW_A 1 238 | 3HUW_N 3HUW_A 1 239 | 3HUW_P 3HUW_A 1 240 | 3HUW_Q 3HUW_A 1 241 | 3HUW_R 3HUW_A 1 242 | 3HUW_S 3HUW_A 1 243 | 3HUW_T 3HUW_A 1 244 | 1P85_C 1P85_0 0 245 | 1FFK_A 1FFK_9 0 246 | 1FFK_C 1FFK_9 0 247 | 1FFK_E 1FFK_9 0 248 | 1FFK_F 1FFK_9 0 249 | 1FFK_G 1FFK_9 0 250 | 1FFK_H 1FFK_9 0 251 | 1FFK_I 1FFK_9 0 252 | 1FFK_J 1FFK_9 0 253 | 1FFK_L 1FFK_9 0 254 | 1FFK_M 1FFK_9 0 255 | 1FFK_O 1FFK_9 0 256 | 1FFK_P 1FFK_9 0 257 | 1FFK_Q 1FFK_9 0 258 | 1FFK_R 1FFK_9 0 259 | 1FFK_S 1FFK_9 0 260 | 1FFK_U 1FFK_9 0 261 | 1FFK_W 1FFK_9 0 262 | 1FFK_X 1FFK_9 0 263 | 1FFK_Y 1FFK_9 0 264 | 1FFK_Z 1FFK_9 0 265 | 1FFK_1 1FFK_9 0 266 | 1GIY_C 1GIY_B 0 267 | 1GIY_D 1GIY_B 0 268 | 1GIY_E 1GIY_B 0 269 | 1GIY_H 1GIY_B 0 270 | 1GIY_I 1GIY_B 0 271 | 1GIY_K 1GIY_B 0 272 | 1GIY_L 1GIY_B 0 273 | 1GIY_N 1GIY_B 0 274 | 1GIY_S 1GIY_B 0 275 | 1GIY_V 1GIY_A 0 276 | 1GIY_X 1GIY_B 0 277 | 1P85_A 1P85_9 0 278 | 1P85_B 1P85_9 0 279 | 1P85_C 1P85_9 0 280 | 1P85_D 1P85_9 0 281 | 1P85_E 1P85_9 0 282 | 1P85_G 1P85_0 0 283 | 1P85_G 1P85_9 0 284 | 1P85_H 1P85_9 0 285 | 1P85_I 1P85_9 0 286 | 1P85_J 1P85_0 0 287 | 1P85_J 1P85_9 0 288 | 1P85_K 1P85_9 0 289 | 1P85_L 1P85_0 0 290 | 1P85_L 1P85_9 0 291 | 1P85_M 1P85_0 0 292 | 1P85_M 1P85_9 0 293 | 1P85_N 1P85_9 0 294 | 1P85_O 1P85_0 0 295 | 1P85_O 1P85_9 0 296 | 1P85_Q 1P85_0 0 297 | 1P85_Q 1P85_9 0 298 | 1P85_R 1P85_9 0 299 | 1P85_S 1P85_9 0 300 | 1P85_W 1P85_9 0 301 | 1P85_X 1P85_9 0 302 | 1P85_Z 1P85_0 0 303 | 1P85_Z 1P85_9 0 304 | 1P85_1 1P85_9 0 305 | 1P85_4 1P85_9 0 306 | 2FTC_K 2FTC_R 0 307 | 2ZJP_A 2ZJP_Z 0 308 | 2ZJP_B 2ZJP_Z 0 309 | 2ZJP_E 2ZJP_Z 0 310 | 2ZJP_F 2ZJP_Z 0 311 | 2ZJP_G 2ZJP_Z 0 312 | 2ZJP_H 2ZJP_Z 0 313 | 2ZJP_I 2ZJP_Z 0 314 | 2ZJP_K 2ZJP_Z 0 315 | 2ZJP_M 2ZJP_Z 0 316 | 2ZJP_N 2ZJP_Z 0 317 | 2ZJP_O 2ZJP_Z 0 318 | 2ZJP_Q 2ZJP_Z 0 319 | 2ZJP_R 2ZJP_Z 0 320 | 2ZJP_U 2ZJP_Z 0 321 | 2ZJP_V 2ZJP_Z 0 322 | 2ZJP_1 2ZJP_Z 0 323 | 2ZJP_2 2ZJP_Z 0 324 | 2ZJP_3 2ZJP_Z 0 325 | 2ZJP_4 2ZJP_Z 0 326 | 2ZKR_a 2ZKR_1 0 327 | 2ZKR_a 2ZKR_F 0 328 | 2ZKR_a 2ZKR_X 0 329 | 2ZKR_a 2ZKR_Y 0 330 | 2ZKR_b 2ZKR_1 0 331 | 2ZKR_b 2ZKR_F 0 332 | 2ZKR_b 2ZKR_X 0 333 | 2ZKR_b 2ZKR_Y 0 334 | 2ZKR_c 2ZKR_F 0 335 | 2ZKR_c 2ZKR_X 0 336 | 2ZKR_c 2ZKR_Y 0 337 | 2ZKR_d 2ZKR_1 0 338 | 2ZKR_d 2ZKR_F 0 339 | 2ZKR_d 2ZKR_X 0 340 | 2ZKR_e 2ZKR_1 0 341 | 2ZKR_e 2ZKR_F 0 342 | 2ZKR_e 2ZKR_X 0 343 | 2ZKR_e 2ZKR_Y 0 344 | 2ZKR_f 2ZKR_1 0 345 | 2ZKR_f 2ZKR_F 0 346 | 2ZKR_f 2ZKR_X 0 347 | 2ZKR_f 2ZKR_Y 0 348 | 2ZKR_g 2ZKR_1 0 349 | 2ZKR_g 2ZKR_F 0 350 | 2ZKR_g 2ZKR_X 0 351 | 2ZKR_g 2ZKR_Y 0 352 | 2ZKR_h 2ZKR_1 0 353 | 2ZKR_h 2ZKR_F 0 354 | 2ZKR_h 2ZKR_X 0 355 | 2ZKR_h 2ZKR_Y 0 356 | 2ZKR_i 2ZKR_1 0 357 | 2ZKR_i 2ZKR_F 0 358 | 2ZKR_i 2ZKR_X 0 359 | 2ZKR_i 2ZKR_Y 0 360 | 2ZKR_j 2ZKR_1 0 361 | 2ZKR_j 2ZKR_F 0 362 | 2ZKR_j 2ZKR_X 0 363 | 2ZKR_j 2ZKR_Y 0 364 | 2ZKR_k 2ZKR_1 0 365 | 2ZKR_k 2ZKR_F 0 366 | 2ZKR_k 2ZKR_X 0 367 | 2ZKR_k 2ZKR_Y 0 368 | 2ZKR_l 2ZKR_1 0 369 | 2ZKR_l 2ZKR_Y 0 370 | 2ZKR_m 2ZKR_F 0 371 | 2ZKR_m 2ZKR_Y 0 372 | 2ZKR_n 2ZKR_1 0 373 | 2ZKR_n 2ZKR_F 0 374 | 2ZKR_n 2ZKR_X 0 375 | 2ZKR_o 2ZKR_1 0 376 | 2ZKR_o 2ZKR_F 0 377 | 2ZKR_o 2ZKR_Y 0 378 | 2ZKR_p 2ZKR_1 0 379 | 2ZKR_p 2ZKR_F 0 380 | 2ZKR_p 2ZKR_Y 0 381 | 2ZKR_q 2ZKR_1 0 382 | 2ZKR_q 2ZKR_F 0 383 | 2ZKR_q 2ZKR_X 0 384 | 2ZKR_r 2ZKR_F 0 385 | 2ZKR_r 2ZKR_Y 0 386 | 2ZKR_s 2ZKR_1 0 387 | 2ZKR_s 2ZKR_F 0 388 | 2ZKR_s 2ZKR_X 0 389 | 2ZKR_s 2ZKR_Y 0 390 | 2ZKR_t 2ZKR_F 0 391 | 2ZKR_t 2ZKR_Y 0 392 | 2ZKR_u 2ZKR_1 0 393 | 2ZKR_u 2ZKR_F 0 394 | 2ZKR_u 2ZKR_X 0 395 | 2ZKR_u 2ZKR_Y 0 396 | 2ZKR_v 2ZKR_0 0 397 | 2ZKR_v 2ZKR_F 0 398 | 2ZKR_v 2ZKR_X 0 399 | 2ZKR_v 2ZKR_Y 0 400 | 2ZKR_w 2ZKR_1 0 401 | 2ZKR_w 2ZKR_F 0 402 | 2ZKR_w 2ZKR_X 0 403 | 2ZKR_x 2ZKR_1 0 404 | 2ZKR_x 2ZKR_F 0 405 | 2ZKR_x 2ZKR_X 0 406 | 2ZKR_x 2ZKR_Y 0 407 | 2ZKR_y 2ZKR_1 0 408 | 2ZKR_y 2ZKR_F 0 409 | 2ZKR_y 2ZKR_X 0 410 | 2ZKR_y 2ZKR_Y 0 411 | 2ZKR_z 2ZKR_1 0 412 | 2ZKR_z 2ZKR_F 0 413 | 2ZKR_z 2ZKR_X 0 414 | 2ZKR_z 2ZKR_Y 0 415 | 2ZKR_2 2ZKR_F 0 416 | 2ZKR_2 2ZKR_X 0 417 | 2ZKR_2 2ZKR_Y 0 418 | 2ZKR_3 2ZKR_F 0 419 | 2ZKR_3 2ZKR_X 0 420 | 2ZKR_3 2ZKR_Y 0 421 | 2ZKR_4 2ZKR_1 0 422 | 2ZKR_4 2ZKR_F 0 423 | 2ZKR_4 2ZKR_X 0 424 | 2ZKR_4 2ZKR_Y 0 425 | 2ZKR_5 2ZKR_1 0 426 | 2ZKR_5 2ZKR_F 0 427 | 2ZKR_5 2ZKR_X 0 428 | 2ZKR_5 2ZKR_Y 0 429 | 2ZKR_6 2ZKR_1 0 430 | 2ZKR_6 2ZKR_F 0 431 | 2ZKR_6 2ZKR_X 0 432 | 2ZKR_6 2ZKR_Y 0 433 | 3BBO_D 3BBO_B 0 434 | 3BBO_D 3BBO_C 0 435 | 3BBO_E 3BBO_B 0 436 | 3BBO_E 3BBO_C 0 437 | 3BBO_F 3BBO_B 0 438 | 3BBO_G 3BBO_B 0 439 | 3BBO_G 3BBO_C 0 440 | 3BBO_H 3BBO_C 0 441 | 3BBO_I 3BBO_B 0 442 | 3BBO_I 3BBO_C 0 443 | 3BBO_J 3BBO_B 0 444 | 3BBO_J 3BBO_C 0 445 | 3BBO_K 3BBO_B 0 446 | 3BBO_K 3BBO_C 0 447 | 3BBO_L 3BBO_B 0 448 | 3BBO_M 3BBO_B 0 449 | 3BBO_M 3BBO_C 0 450 | 3BBO_N 3BBO_B 0 451 | 3BBO_N 3BBO_C 0 452 | 3BBO_O 3BBO_C 0 453 | 3BBO_P 3BBO_B 0 454 | 3BBO_Q 3BBO_C 0 455 | 3BBO_R 3BBO_B 0 456 | 3BBO_S 3BBO_B 0 457 | 3BBO_S 3BBO_C 0 458 | 3BBO_T 3BBO_B 0 459 | 3BBO_T 3BBO_C 0 460 | 3BBO_U 3BBO_B 0 461 | 3BBO_U 3BBO_C 0 462 | 3BBO_V 3BBO_B 0 463 | 3BBO_V 3BBO_C 0 464 | 3BBO_W 3BBO_B 0 465 | 3BBO_W 3BBO_C 0 466 | 3BBO_X 3BBO_C 0 467 | 3BBO_Y 3BBO_B 0 468 | 3BBO_Y 3BBO_C 0 469 | 3BBO_Z 3BBO_B 0 470 | 3BBO_Z 3BBO_C 0 471 | 3BBO_1 3BBO_A 0 472 | 3BBO_1 3BBO_B 0 473 | 3BBO_1 3BBO_C 0 474 | 3BBO_2 3BBO_B 0 475 | 3BBO_3 3BBO_B 0 476 | 3BBO_3 3BBO_C 0 477 | 3BBO_4 3BBO_B 0 478 | 3BBO_4 3BBO_C 0 479 | 3BBO_5 3BBO_B 0 480 | 3BBO_5 3BBO_C 0 481 | 3BBO_6 3BBO_B 0 482 | 3BBO_6 3BBO_C 0 483 | 3CW1_D 3CW1_V 0 484 | 3CW1_C 3CW1_V 0 485 | 3CW1_F 3CW1_V 0 486 | 3CW1_E 3CW1_V 0 487 | 3CW1_G 3CW1_V 0 488 | 3CW1_K 3CW1_V 0 489 | -------------------------------------------------------------------------------- /data/RPI369_pairs.txt: -------------------------------------------------------------------------------- 1 | 1drz-A 1drz-B 1 2 | 2r93-B 2r93-R 1 3 | 2r93-A 2r93-R 1 4 | 1vfg-A 1vfg-C 1 5 | 1jbt-A 1jbt-C 1 6 | 1ec6-B 1ec6-C 1 7 | 1f8v-C 1f8v-R 1 8 | 1f8v-E 1f8v-R 1 9 | 3cw1-G 3cw1-V 1 10 | 3cw1-B 3cw1-V 1 11 | 3cw1-T 3cw1-w 1 12 | 3cw1-P 3cw1-v 1 13 | 3cw1-Y 3cw1-x 1 14 | 3cw1-2 3cw1-x 1 15 | 3cw1-5 3cw1-x 1 16 | 3cw1-K 3cw1-V 1 17 | 3cw1-J 3cw1-x 1 18 | 3cw1-9 3cw1-v 1 19 | 1hq1-A 1hq1-B 1 20 | 1dul-A 1dul-B 1 21 | 2nqp-C 2nqp-F 1 22 | 2az2-A 2az2-C 1 23 | 2hgh-A 2hgh-B 1 24 | 2r92-B 2r92-T 1 25 | 2r92-A 2r92-T 1 26 | 2b2d-C 2b2d-S 1 27 | 2izn-A 2izn-R 1 28 | 1r3e-A 1r3e-E 1 29 | 3ks8-A 3ks8-F 1 30 | 2b2e-C 2b2e-R 1 31 | 2xfz-Y 2xfz-X 1 32 | 2wri-Y 2wri-X 1 33 | 1ze2-B 1ze2-D 1 34 | 1ze2-A 1ze2-C 1 35 | 3mum-P 3mum-R 1 36 | 3mur-P 3mur-R 1 37 | 1x1l-X 1x1l-A 1 38 | 1rpu-B 1rpu-D 1 39 | 2zh4-A 2zh4-B 1 40 | 2ply-A 2ply-C 1 41 | 1wsu-A 1wsu-G 1 42 | 1urn-C 1urn-R 1 43 | 2zh3-A 2zh3-B 1 44 | 2jpp-B 2jpp-D 1 45 | 2wrn-Z 2wrn-Y 1 46 | 3cz3-A 3cz3-E 1 47 | 2cv1-B 2cv1-D 1 48 | 3cz3-B 3cz3-G 1 49 | 1aq4-C 1aq4-S 1 50 | 2c4y-C 2c4y-S 1 51 | 1fje-B 1fje-A 1 52 | 3kfu-H 3kfu-N 1 53 | 3kfu-B 3kfu-N 1 54 | 3kfu-F 3kfu-L 1 55 | 3kfu-G 3kfu-L 1 56 | 1qzw-A 1qzw-B 1 57 | 2dlc-X 2dlc-Y 1 58 | 1zdh-A 1zdh-R 1 59 | 1p6v-C 1p6v-B 1 60 | 2gje-D 2gje-R 1 61 | 2gje-A 2gje-R 1 62 | 2iz8-A 2iz8-R 1 63 | 2wyy-J 2wyy-S 1 64 | 2fk6-A 2fk6-R 1 65 | 1q2r-C 1q2r-F 1 66 | 3deg-C 3deg-G 1 67 | 1a9n-D 1a9n-R 1 68 | 1a9n-A 1a9n-Q 1 69 | 2iy3-A 2iy3-B 1 70 | 2pxv-A 2pxv-B 1 71 | 1urn-A 1urn-P 1 72 | 3htx-D 3htx-E 1 73 | 3ciy-A 3ciy-D 1 74 | 3ktv-B 3ktv-A 1 75 | 2d6f-D 2d6f-F 1 76 | 3hjy-A 3hjy-C 1 77 | 2c50-A 2c50-R 1 78 | 1h3e-A 1h3e-B 1 79 | 1f6u-A 1f6u-B 1 80 | 2j0q-I 2j0q-H 1 81 | 2j0q-B 2j0q-H 1 82 | 1aud-A 1aud-B 1 83 | 1jid-A 1jid-B 1 84 | 1kog-D 1kog-K 1 85 | 1zbh-D 1zbh-F 1 86 | 1q2s-A 1q2s-E 1 87 | 2nuf-A 2nuf-C 1 88 | 2b3j-C 2b3j-G 1 89 | 2xlj-A 2xlj-B 1 90 | 2b2d-C 2b2d-R 1 91 | 2der-B 2der-D 1 92 | 3iab-A 3iab-R 1 93 | 3iab-B 3iab-R 1 94 | 2xli-A 2xli-B 1 95 | 1zdi-A 1zdi-R 1 96 | 3epl-B 3epl-E 1 97 | 2jea-B 2jea-C 1 98 | 2jea-A 2jea-C 1 99 | 1u1y-B 1u1y-S 1 100 | 2csx-A 2csx-D 1 101 | 1ry1-D 1ry1-M 1 102 | 1ry1-C 1ry1-M 1 103 | 5msf-C 5msf-S 1 104 | 3foz-B 3foz-C 1 105 | 1jbr-A 1jbr-C 1 106 | 2fy1-A 2fy1-B 1 107 | 2c4z-A 2c4z-R 1 108 | 1qf6-A 1qf6-B 1 109 | 2ake-A 2ake-B 1 110 | 3ftf-A 3ftf-D 1 111 | 2b9m-Y 2b9m-X 1 112 | 2xlk-B 2xlk-D 1 113 | 2iy5-B 2iy5-T 1 114 | 1eiy-A 1eiy-C 1 115 | 3hl2-B 3hl2-E 1 116 | 1t4l-B 1t4l-A 1 117 | 3cz3-D 3cz3-F 1 118 | 2ob7-B 2ob7-A 1 119 | 1aq3-C 1aq3-R 1 120 | 2izm-C 2izm-S 1 121 | 2c51-C 2c51-S 1 122 | 1j2b-B 1j2b-C 1 123 | 2zh9-A 2zh9-B 1 124 | 3boy-C 3boy-D 1 125 | 5msf-A 5msf-R 1 126 | 2b3j-C 2b3j-H 1 127 | 2az2-B 2az2-D 1 128 | 3iwn-D 3iwn-B 1 129 | 2dra-A 2dra-B 1 130 | 2f8k-A 2f8k-B 1 131 | 1k8w-A 1k8w-B 1 132 | 2izm-C 2izm-R 1 133 | 1u6p-A 1u6p-B 1 134 | 1exd-A 1exd-B 1 135 | 2gjw-C 2gjw-I 1 136 | 3dkn-A 3dkn-F 1 137 | 3akz-B 3akz-E 1 138 | 3al0-B 3al0-E 1 139 | 3al0-C 3al0-E 1 140 | 2bu1-C 2bu1-S 1 141 | 2bs0-C 2bs0-S 1 142 | 2j0s-T 2j0s-E 1 143 | 2j0s-A 2j0s-E 1 144 | 1xok-C 1xok-A 1 145 | 1l1c-A 1l1c-C 1 146 | 2bq5-A 2bq5-R 1 147 | 2pxf-A 2pxf-B 1 148 | 3a2k-B 3a2k-D 1 149 | 2gjw-B 2gjw-E 1 150 | 2ese-A 2ese-B 1 151 | 1aq3-C 1aq3-S 1 152 | 3ivk-B 3ivk-C 1 153 | 2dr5-A 2dr5-B 1 154 | 2oj3-A 2oj3-B 1 155 | 2b63-B 2b63-R 1 156 | 2b63-A 2b63-R 1 157 | 1aq4-A 1aq4-R 1 158 | 1ekz-A 1ekz-B 1 159 | 1zdi-B 1zdi-S 1 160 | 2w2h-A 2w2h-S 1 161 | 2w2h-D 2w2h-S 1 162 | 2jpp-B 2jpp-C 1 163 | 1j1u-A 1j1u-B 1 164 | 1b23-P 1b23-R 1 165 | 1u0b-B 1u0b-A 1 166 | 2nue-B 2nue-C 1 167 | 2nr0-C 2nr0-H 1 168 | 2ez6-A 2ez6-C 1 169 | 1zbh-A 1zbh-E 1 170 | 3d5c-X 3d5c-V 1 171 | 2b6g-A 2b6g-B 1 172 | 3eq4-X 3eq4-Y 1 173 | 2az0-B 2az0-D 1 174 | 1il2-A 1il2-D 1 175 | 1asz-B 1asz-R 1 176 | 1r3e-A 1r3e-C 1 177 | 2b64-Y 2b64-X 1 178 | 1q2s-B 1q2s-F 1 179 | 2pxu-A 2pxu-B 1 180 | 3deg-C 3deg-I 1 181 | 2zh1-A 2zh1-B 1 182 | 2c4z-C 2c4z-S 1 183 | 3a6p-H 3a6p-J 1 184 | 3a6p-F 3a6p-J 1 185 | 2b3j-A 2b3j-F 1 186 | 2b2g-C 2b2g-S 1 187 | 2c4q-C 2c4q-S 1 188 | 2ab4-A 2ab4-B 1 189 | 3dkn-A 3dkn-D 1 190 | 3eq3-X 3eq3-Y 1 191 | 2rfk-A 2rfk-E 1 192 | 2wyy-F 2wyy-R 1 193 | 2bny-B 2bny-R 1 194 | 1m5k-C 1m5k-B 1 195 | 2j28-9 2j28-8 1 196 | 1e8s-B 1e8s-C 1 197 | 1e8s-A 1e8s-C 1 198 | 3k0j-D 3k0j-F 1 199 | 2ozb-D 2ozb-F 1 200 | 2ozb-B 2ozb-C 1 201 | 1r9f-A 1r9f-B 1 202 | 2xb2-T 2xb2-E 1 203 | 2xb2-A 2xb2-E 1 204 | 2drb-A 2drb-B 1 205 | 1ob5-C 1ob5-B 1 206 | 2b2g-C 2b2g-R 1 207 | 2c50-C 2c50-S 1 208 | 2zhb-A 2zhb-B 1 209 | 1urn-B 1urn-Q 1 210 | 3ktw-A 3ktw-C 1 211 | 2du4-A 2du4-C 1 212 | 2dr2-A 2dr2-B 1 213 | 2azx-A 2azx-C 1 214 | 2pxe-A 2pxe-B 1 215 | 2bte-D 2bte-E 1 216 | 2x7n-B 2x7n-A 1 217 | 1u1y-A 1u1y-R 1 218 | 3cz3-D 3cz3-H 1 219 | 2bs1-B 2bs1-R 1 220 | 1rpu-A 1rpu-C 1 221 | 3mr8-V 3mr8-W 1 222 | 3kix-y 3kix-v 1 223 | 3huy-V 3huy-W 1 224 | 2fmt-B 2fmt-C 1 225 | 2xfz-Y 2xfz-V 1 226 | 2wrk-Y 2wrk-V 1 227 | 1sjf-A 1sjf-B 1 228 | 1vbx-A 1vbx-B 1 229 | 1vby-A 1vby-B 1 230 | 2az0-A 2az0-C 1 231 | 2bny-C 2bny-S 1 232 | 2v0g-A 2v0g-F 1 233 | 3id5-A 3id5-D 1 234 | 2anr-A 2anr-B 1 235 | 2pjp-A 2pjp-B 1 236 | 1ser-B 1ser-T 1 237 | 2pxl-A 2pxl-B 1 238 | 1h4s-A 1h4s-T 1 239 | 1hvu-K 1hvu-L 1 240 | 1hvu-A 1hvu-F 1 241 | 1hvu-D 1hvu-C 1 242 | 1rkj-A 1rkj-B 1 243 | 1dz5-A 1dz5-C 1 244 | 2gtt-T 2gtt-X 1 245 | 2bu1-C 2bu1-R 1 246 | 2gtt-D 2gtt-W 1 247 | 2iz9-C 2iz9-R 1 248 | 2pxp-A 2pxp-B 1 249 | 2zzn-A 2zzn-D 1 250 | 1qu3-A 1qu3-T 1 251 | 1r9f-A 1r9f-C 1 252 | 2xlk-A 2xlk-C 1 253 | 1qfq-B 1qfq-A 1 254 | 1efw-B 1efw-D 1 255 | 1c0a-A 1c0a-B 1 256 | 2hvy-A 2hvy-E 1 257 | 2bs0-C 2bs0-R 1 258 | 3hax-A 3hax-E 1 259 | 2f8s-A 2f8s-C 1 260 | 2go5-W 2go5-A 1 261 | 2go5-B 2go5-A 1 262 | 2go5-2 2go5-A 1 263 | 2go5-1 2go5-A 1 264 | 2bx2-L 2bx2-R 1 265 | 1gtn-L 1gtn-W 1 266 | 1gtn-C 1gtn-W 1 267 | 1a1t-A 1a1t-B 1 268 | 1f7u-A 1f7u-B 1 269 | 2bs1-C 2bs1-S 1 270 | 3a6p-C 3a6p-D 1 271 | 3a6p-F 3a6p-I 1 272 | 1gax-A 1gax-C 1 273 | 1zjw-A 1zjw-B 1 274 | 3kiu-y 3kiu-a 1 275 | 3hjy-A 3hjy-D 1 276 | 1r3e-A 1r3e-D 1 277 | 3hhn-D 3hhn-E 1 278 | 3htx-A 3htx-C 1 279 | 2pxt-A 2pxt-B 1 280 | 2i91-B 2i91-F 1 281 | 2zh7-A 2zh7-B 1 282 | 1ec6-A 1ec6-D 1 283 | 1wz2-B 1wz2-D 1 284 | 2zzm-A 2zzm-B 1 285 | 2c51-C 2c51-R 1 286 | 3g8s-D 3g8s-R 1 287 | 2b3j-B 2b3j-E 1 288 | 1lng-A 1lng-B 1 289 | 3dd2-H 3dd2-B 1 290 | 3dd2-L 3dd2-B 1 291 | 2r8s-L 2r8s-R 1 292 | 2pxq-A 2pxq-B 1 293 | 3kis-y 3kis-x 1 294 | 1zdk-B 1zdk-S 1 295 | 2xb2-S 2xb2-R 1 296 | 2xb2-X 2xb2-R 1 297 | 1q2r-D 1q2r-E 1 298 | 2iz8-C 2iz8-S 1 299 | 1ry1-D 1ry1-E 1 300 | 1ry1-C 1ry1-E 1 301 | 2zh2-A 2zh2-B 1 302 | 2ct8-B 2ct8-D 1 303 | 2zh6-A 2zh6-B 1 304 | 1ry1-D 1ry1-N 1 305 | 1ry1-C 1ry1-N 1 306 | 2c4q-B 2c4q-R 1 307 | 1gtf-L 1gtf-W 1 308 | 2b2e-C 2b2e-S 1 309 | 2r92-B 2r92-P 1 310 | 2r92-A 2r92-P 1 311 | 1e7k-B 1e7k-C 1 312 | 1zdk-A 1zdk-R 1 313 | 1mfq-C 1mfq-A 1 314 | 1ry1-W 1ry1-A 1 315 | 1ry1-B 1ry1-A 1 316 | 1l9a-A 1l9a-B 1 317 | 2j37-W 2j37-A 1 318 | 2i91-A 2i91-D 1 319 | 2czj-G 2czj-H 1 320 | 3deg-C 3deg-J 1 321 | 2zh8-A 2zh8-B 1 322 | 3bo3-A 3bo3-B 1 323 | 3hhz-N 3hhz-R 1 324 | 1zse-A 1zse-R 1 325 | 3dkn-A 3dkn-E 1 326 | 2zni-A 2zni-C 1 327 | 2bq5-C 2bq5-S 1 328 | 3egz-A 3egz-B 1 329 | 2zko-A 2zko-C 1 330 | 2zi0-B 2zi0-D 1 331 | 1ooa-A 1ooa-C 1 332 | 1jbr-B 1jbr-D 1 333 | 3foz-B 3foz-D 1 334 | 2iz9-C 2iz9-S 1 335 | 3cun-A 3cun-C 1 336 | 3cul-A 3cul-C 1 337 | 2j0q-T 2j0q-E 1 338 | 2j0q-A 2j0q-E 1 339 | 3ks8-A 3ks8-E 1 340 | 2dr7-A 2dr7-B 1 341 | 2gic-E 2gic-R 1 342 | 2ipy-A 2ipy-D 1 343 | 3icq-C 3icq-E 1 344 | 3icq-U 3icq-E 1 345 | 2rkj-N 2rkj-O 1 346 | 3moj-B 3moj-A 1 347 | 1hji-B 1hji-A 1 348 | 1qrt-A 1qrt-B 1 349 | 1c9s-F 1c9s-W 1 350 | 2v3c-D 2v3c-M 1 351 | 2v3c-A 2v3c-M 1 352 | 2wh1-Y 2wh1-V 1 353 | 2zxu-A 2zxu-C 1 354 | 2ann-A 2ann-B 1 355 | 1zdh-B 1zdh-S 1 356 | 3hay-A 3hay-E 1 357 | 2zuf-A 2zuf-B 1 358 | 2izn-C 2izn-S 1 359 | 3hjw-A 3hjw-D 1 360 | 2c4y-A 2c4y-R 1 361 | 1ttt-B 1ttt-D 1 362 | 1ml5-Z 1ml5-B 1 363 | 3deg-C 3deg-A 1 364 | 1ttt-A 1ttt-D 1 365 | 1sz1-B 1sz1-F 1 366 | 1zo1-I 1zo1-F 1 367 | 3adc-A 3adc-C 1 368 | 2f8t-A 2f8t-D 1 369 | 2ihx-A 2ihx-B 1 370 | 1drz-A 3dkn-F 0 371 | 2r93-B 3cw1-V 0 372 | 2r93-A 1zbh-E 0 373 | 1vfg-A 2ez6-C 0 374 | 1jbt-A 2nuf-C 0 375 | 1ec6-B 2xlk-C 0 376 | 1f8v-C 3foz-C 0 377 | 1f8v-E 2bs0-R 0 378 | 3cw1-G 1urn-Q 0 379 | 3cw1-B 3bo3-B 0 380 | 3cw1-T 3adc-C 0 381 | 3cw1-P 3epl-E 0 382 | 3cw1-Y 2r92-P 0 383 | 3cw1-2 1asz-R 0 384 | 3cw1-5 2zzm-B 0 385 | 3cw1-K 3deg-G 0 386 | 3cw1-J 1urn-R 0 387 | 3cw1-9 2zko-C 0 388 | 1hq1-A 2iz8-R 0 389 | 1dul-A 2c4q-R 0 390 | 2nqp-C 2gtt-W 0 391 | 2az2-A 2bny-S 0 392 | 2hgh-A 1ec6-D 0 393 | 2r92-B 1drz-B 0 394 | 2r92-A 2gtt-X 0 395 | 2b2d-C 1vby-B 0 396 | 2izn-A 2xfz-X 0 397 | 1r3e-A 2zh3-B 0 398 | 3ks8-A 1q2r-E 0 399 | 2b2e-C 2drb-B 0 400 | 2xfz-Y 2f8k-B 0 401 | 2wri-Y 2j37-A 0 402 | 1ze2-B 1f8v-R 0 403 | 1ze2-A 2r8s-R 0 404 | 3mum-P 2xlj-B 0 405 | 3mur-P 1rpu-C 0 406 | 1x1l-X 2zh4-B 0 407 | 1rpu-B 3a6p-J 0 408 | 2zh4-A 1hvu-C 0 409 | 2ply-A 1zjw-B 0 410 | 1wsu-A 2c4y-R 0 411 | 1urn-C 2der-D 0 412 | 2zh3-A 2bx2-R 0 413 | 2jpp-B 1qzw-B 0 414 | 2wrn-Z 2b2e-R 0 415 | 3cz3-A 3deg-G 0 416 | 2cv1-B 3id5-D 0 417 | 3cz3-B 2ply-C 0 418 | 1aq4-C 1il2-D 0 419 | 2c4y-C 2zhb-B 0 420 | 1fje-B 2zh2-B 0 421 | 3kfu-H 1q2r-F 0 422 | 3kfu-B 2b2e-R 0 423 | 3kfu-F 2izn-S 0 424 | 3kfu-G 1f7u-B 0 425 | 1qzw-A 2ihx-B 0 426 | 2dlc-X 2f8k-B 0 427 | 1zdh-A 1qfq-A 0 428 | 1p6v-C 1ry1-M 0 429 | 2gje-D 2bu1-R 0 430 | 2gje-A 3deg-A 0 431 | 2iz8-A 1q2r-F 0 432 | 2wyy-J 1vby-B 0 433 | 2fk6-A 1b23-R 0 434 | 1q2r-C 2ct8-D 0 435 | 3deg-C 3mr8-W 0 436 | 1a9n-D 2bs1-R 0 437 | 1a9n-A 2j0q-E 0 438 | 2iy3-A 2izn-R 0 439 | 2pxv-A 2j37-A 0 440 | 1urn-A 3ks8-E 0 441 | 3htx-D 2pxp-B 0 442 | 3ciy-A 3ktw-C 0 443 | 3ktv-B 1urn-Q 0 444 | 2d6f-D 2wrk-V 0 445 | 3hjy-A 2x7n-A 0 446 | 2c50-A 1exd-B 0 447 | 1h3e-A 2nr0-H 0 448 | 1f6u-A 2zh2-B 0 449 | 2j0q-I 1vbx-B 0 450 | 2j0q-B 1f7u-B 0 451 | 1aud-A 3id5-D 0 452 | 1jid-A 2hgh-B 0 453 | 1kog-D 1r9f-B 0 454 | 1zbh-D 2xlk-C 0 455 | 1q2s-A 3a6p-D 0 456 | 2nuf-A 2c4z-R 0 457 | 2b3j-C 2csx-D 0 458 | 2xlj-A 2j0q-E 0 459 | 2b2d-C 2zuf-B 0 460 | 2der-B 3deg-J 0 461 | 3iab-A 3mr8-W 0 462 | 3iab-B 3hay-E 0 463 | 2xli-A 3hjy-D 0 464 | 1zdi-A 3ivk-C 0 465 | 3epl-B 1ob5-B 0 466 | 2jea-B 2ply-C 0 467 | 2jea-A 3icq-E 0 468 | 1u1y-B 3adc-C 0 469 | 2csx-A 2hgh-B 0 470 | 1ry1-D 1qf6-B 0 471 | 1ry1-C 2az2-C 0 472 | 5msf-C 2pxf-B 0 473 | 3foz-B 2fk6-R 0 474 | 1jbr-A 1r3e-C 0 475 | 2fy1-A 1zdh-R 0 476 | 2c4z-A 3mr8-W 0 477 | 1qf6-A 2iz8-S 0 478 | 2ake-A 2pxt-B 0 479 | 3ftf-A 1ec6-D 0 480 | 2b9m-Y 2b3j-G 0 481 | 2xlk-B 2gjw-E 0 482 | 2iy5-B 1l9a-B 0 483 | 1eiy-A 1sz1-F 0 484 | 3hl2-B 2zzm-B 0 485 | 1t4l-B 1q2s-E 0 486 | 3cz3-D 1r3e-E 0 487 | 2ob7-B 2iy3-B 0 488 | 1aq3-C 1aud-B 0 489 | 2izm-C 1ry1-N 0 490 | 2c51-C 1h3e-B 0 491 | 1j2b-B 1jbr-D 0 492 | 2zh9-A 2anr-B 0 493 | 3boy-C 2gjw-I 0 494 | 5msf-A 2wyy-S 0 495 | 2b3j-C 2bu1-R 0 496 | 2az2-B 2j37-A 0 497 | 3iwn-D 1efw-D 0 498 | 2dra-A 2c4y-R 0 499 | 2f8k-A 2gje-R 0 500 | 1k8w-A 2jea-C 0 501 | 2izm-C 3k0j-F 0 502 | 1u6p-A 2wyy-S 0 503 | 1exd-A 2xfz-V 0 504 | 2gjw-C 1e8s-C 0 505 | 3dkn-A 2der-D 0 506 | 3akz-B 3hl2-E 0 507 | 3al0-B 3kix-v 0 508 | 3al0-C 2ese-B 0 509 | 2bu1-C 2ct8-D 0 510 | 2bs0-C 3icq-E 0 511 | 2j0s-T 2iz9-R 0 512 | 2j0s-A 2j37-A 0 513 | 1xok-C 1hvu-F 0 514 | 1l1c-A 2ipy-D 0 515 | 2bq5-A 1b23-R 0 516 | 2pxf-A 1u0b-A 0 517 | 3a2k-B 2zh6-B 0 518 | 2gjw-B 1zdi-R 0 519 | 2ese-A 2f8s-C 0 520 | 1aq3-C 2xlj-B 0 521 | 3ivk-B 2j37-A 0 522 | 2dr5-A 2wri-X 0 523 | 2oj3-A 3eq3-Y 0 524 | 2b63-B 2bs0-S 0 525 | 2b63-A 1j2b-C 0 526 | 1aq4-A 2azx-C 0 527 | 1ekz-A 1ob5-B 0 528 | 1zdi-B 1e7k-C 0 529 | 2w2h-A 3eq4-Y 0 530 | 2w2h-D 2azx-C 0 531 | 2jpp-B 1q2r-F 0 532 | 1j1u-A 1wz2-D 0 533 | 1b23-P 3a6p-I 0 534 | 1u0b-B 2ihx-B 0 535 | 2nue-B 2f8t-D 0 536 | 2nr0-C 1hq1-B 0 537 | 2ez6-A 1dul-B 0 538 | 1zbh-A 1ze2-C 0 539 | 3d5c-X 3cw1-v 0 540 | 2b6g-A 2f8t-D 0 541 | 3eq4-X 1vfg-C 0 542 | 2az0-B 1dul-B 0 543 | 1il2-A 2xlk-D 0 544 | 1asz-B 2gtt-X 0 545 | 1r3e-A 1hvu-C 0 546 | 2b64-Y 2zh6-B 0 547 | 1q2s-B 2xlj-B 0 548 | 2pxu-A 1q2s-E 0 549 | 3deg-C 2xfz-X 0 550 | 2zh1-A 3huy-W 0 551 | 2c4z-C 1l9a-B 0 552 | 3a6p-H 3mur-R 0 553 | 3a6p-F 1c0a-B 0 554 | 2b3j-A 2j0s-E 0 555 | 2b2g-C 3htx-C 0 556 | 2c4q-C 1lng-B 0 557 | 2ab4-A 2bs0-R 0 558 | 3dkn-A 2drb-B 0 559 | 3eq3-X 3hjw-D 0 560 | 2rfk-A 2b9m-X 0 561 | 2wyy-F 2c4z-R 0 562 | 2bny-B 1dz5-C 0 563 | 1m5k-C 2bq5-R 0 564 | 2j28-9 1xok-A 0 565 | 1e8s-B 1c9s-W 0 566 | 1e8s-A 1urn-P 0 567 | 3k0j-D 2bx2-R 0 568 | 2ozb-D 2x7n-A 0 569 | 2ozb-B 2rkj-O 0 570 | 1r9f-A 2ipy-D 0 571 | 2xb2-T 2az0-C 0 572 | 2xb2-A 3bo3-B 0 573 | 2drb-A 3ks8-F 0 574 | 1ob5-C 3htx-E 0 575 | 2b2g-C 2ez6-C 0 576 | 2c50-C 3mur-R 0 577 | 2zhb-A 1f8v-R 0 578 | 1urn-B 2v0g-F 0 579 | 3ktw-A 1zdh-S 0 580 | 2du4-A 3moj-A 0 581 | 2dr2-A 3mr8-W 0 582 | 2azx-A 1ry1-E 0 583 | 2pxe-A 1aud-B 0 584 | 2bte-D 1l1c-C 0 585 | 2x7n-B 1t4l-A 0 586 | 1u1y-A 3dkn-E 0 587 | 3cz3-D 2bny-S 0 588 | 2bs1-B 3hjy-D 0 589 | 1rpu-A 2dr7-B 0 590 | 3mr8-V 1e7k-C 0 591 | 3kix-y 2zh4-B 0 592 | 3huy-V 1l9a-B 0 593 | 2fmt-B 1ooa-C 0 594 | 2xfz-Y 1x1l-A 0 595 | 2wrk-Y 2iz9-S 0 596 | 1sjf-A 3boy-D 0 597 | 1vbx-A 1ec6-D 0 598 | 1vby-A 3iab-R 0 599 | 2az0-A 2zxu-C 0 600 | 2bny-C 1p6v-B 0 601 | 2v0g-A 2b2g-S 0 602 | 3id5-A 2xfz-V 0 603 | 2anr-A 3cz3-F 0 604 | 2pjp-A 1zo1-F 0 605 | 1ser-B 2ese-B 0 606 | 2pxl-A 3icq-E 0 607 | 1h4s-A 2jpp-D 0 608 | 1hvu-K 1zdi-R 0 609 | 1hvu-A 2ann-B 0 610 | 1hvu-D 2bs0-S 0 611 | 1rkj-A 2b3j-E 0 612 | 1dz5-A 1eiy-C 0 613 | 2gtt-T 2izn-R 0 614 | 2bu1-C 1hvu-F 0 615 | 2gtt-D 2bs1-S 0 616 | 2iz9-C 2f8k-B 0 617 | 2pxp-A 3d5c-V 0 618 | 2zzn-A 3al0-E 0 619 | 1qu3-A 2zzn-D 0 620 | 1r9f-A 2der-D 0 621 | 2xlk-A 1e7k-C 0 622 | 1qfq-B 2rfk-E 0 623 | 1efw-B 1wsu-G 0 624 | 1c0a-A 2xfz-X 0 625 | 2hvy-A 5msf-R 0 626 | 2bs0-C 2w2h-S 0 627 | 3hax-A 2nue-C 0 628 | 2f8s-A 1qu3-T 0 629 | 2go5-W 1zo1-F 0 630 | 2go5-B 2xb2-R 0 631 | 2go5-2 2r92-P 0 632 | 2go5-1 2b2g-S 0 633 | 2bx2-L 1wsu-G 0 634 | 1gtn-L 2r92-T 0 635 | 1gtn-C 1hvu-C 0 636 | 1a1t-A 2ihx-B 0 637 | 1f7u-A 2wyy-S 0 638 | 2bs1-C 2ese-B 0 639 | 3a6p-C 1j2b-C 0 640 | 3a6p-F 2zuf-B 0 641 | 1gax-A 2c51-R 0 642 | 1zjw-A 2zh8-B 0 643 | 3kiu-y 2gtt-W 0 644 | 3hjy-A 2wri-X 0 645 | 1r3e-A 2c51-S 0 646 | 3hhn-D 1l1c-C 0 647 | 3htx-A 1a9n-R 0 648 | 2pxt-A 1sjf-B 0 649 | 2i91-B 2ct8-D 0 650 | 2zh7-A 3deg-J 0 651 | 1ec6-A 2zko-C 0 652 | 1wz2-B 2ann-B 0 653 | 2zzm-A 1r3e-E 0 654 | 2c51-C 3cz3-G 0 655 | 3g8s-D 2gtt-X 0 656 | 2b3j-B 1e7k-C 0 657 | 1lng-A 1r3e-D 0 658 | 3dd2-H 1f7u-B 0 659 | 3dd2-L 1ser-T 0 660 | 2r8s-L 1gtn-W 0 661 | 2pxq-A 1ec6-C 0 662 | 3kis-y 3ciy-D 0 663 | 1zdk-B 2bx2-R 0 664 | 2xb2-S 3hay-E 0 665 | 2xb2-X 2b2d-S 0 666 | 1q2r-D 3akz-E 0 667 | 2iz8-C 2iy5-T 0 668 | 1ry1-D 3eq4-Y 0 669 | 1ry1-C 2dlc-Y 0 670 | 2zh2-A 3hax-E 0 671 | 2ct8-B 2xfz-X 0 672 | 2zh6-A 1zse-R 0 673 | 1ry1-D 3kix-v 0 674 | 1ry1-C 2zh4-B 0 675 | 2c4q-B 2zzm-B 0 676 | 1gtf-L 2bs0-S 0 677 | 2b2e-C 3id5-D 0 678 | 2r92-B 3hl2-E 0 679 | 2r92-A 1zdh-S 0 680 | 1e7k-B 2c4y-R 0 681 | 1zdk-A 3a6p-D 0 682 | 1mfq-C 1urn-P 0 683 | 1ry1-W 2izm-S 0 684 | 1ry1-B 2zzm-B 0 685 | 1l9a-A 1jbr-C 0 686 | 2j37-W 2jpp-C 0 687 | 2i91-A 1r3e-C 0 688 | 2czj-G 2izm-S 0 689 | 3deg-C 2wyy-S 0 690 | 2zh8-A 3kfu-N 0 691 | 3bo3-A 1gtn-W 0 692 | 3hhz-N 2f8k-B 0 693 | 1zse-A 2anr-B 0 694 | 3dkn-A 2c50-R 0 695 | 2zni-A 1zdi-R 0 696 | 2bq5-C 1rpu-C 0 697 | 3egz-A 1r9f-B 0 698 | 2zko-A 1e8s-C 0 699 | 2zi0-B 1il2-D 0 700 | 1ooa-A 2bx2-R 0 701 | 1jbr-B 2zi0-D 0 702 | 3foz-B 3g8s-R 0 703 | 2iz9-C 2zko-C 0 704 | 3cun-A 1rkj-B 0 705 | 3cul-A 2ipy-D 0 706 | 2j0q-T 1zbh-E 0 707 | 2j0q-A 3icq-E 0 708 | 3ks8-A 2bq5-R 0 709 | 2dr7-A 3ciy-D 0 710 | 2gic-E 1qzw-B 0 711 | 2ipy-A 1l9a-B 0 712 | 3icq-C 3ktv-A 0 713 | 3icq-U 1hvu-L 0 714 | 2rkj-N 2dlc-Y 0 715 | 3moj-B 1x1l-A 0 716 | 1hji-B 2anr-B 0 717 | 1qrt-A 3kfu-L 0 718 | 1c9s-F 3deg-A 0 719 | 2v3c-D 3hax-E 0 720 | 2v3c-A 3htx-C 0 721 | 2wh1-Y 3ivk-C 0 722 | 2zxu-A 1ob5-B 0 723 | 2ann-A 2bq5-S 0 724 | 1zdh-B 1fje-A 0 725 | 3hay-A 3deg-A 0 726 | 2zuf-A 1a9n-R 0 727 | 2izn-C 2gtt-X 0 728 | 3hjw-A 2pxl-B 0 729 | 2c4y-A 3ktw-C 0 730 | 1ttt-B 1c0a-B 0 731 | 1ml5-Z 1u6p-B 0 732 | 3deg-C 1qzw-B 0 733 | 1ttt-A 2zh9-B 0 734 | 1sz1-B 1vby-B 0 735 | 1zo1-I 1r9f-B 0 736 | 3adc-A 2f8k-B 0 737 | 2f8t-A 3foz-C 0 738 | 2ihx-A 1a1t-B 0 739 | -------------------------------------------------------------------------------- /sample/utils/sequence_encoder.py: -------------------------------------------------------------------------------- 1 | import math 2 | import string 3 | from functools import reduce 4 | 5 | import numpy as np 6 | 7 | 8 | # encoder for protein sequence 9 | class ProEncoder: 10 | elements = 'AIYHRDC' 11 | structs = 'hec' 12 | 13 | element_number = 7 14 | # number of structure kind 15 | struct_kind = 3 16 | 17 | # clusters: {A,G,V}, {I,L,F,P}, {Y,M,T,S}, {H,N,Q,W}, {R,K}, {D,E}, {C} 18 | pro_intab = 'AGVILFPYMTSHNQWRKDEC' 19 | pro_outtab = 'AAAIIIIYYYYHHHHRRDDC' 20 | 21 | def __init__(self, WINDOW_P_UPLIMIT, WINDOW_P_STRUCT_UPLIMIT, CODING_FREQUENCY, VECTOR_REPETITION_CNN, 22 | TRUNCATION_LEN=None, PERIOD_EXTENDED=None): 23 | 24 | self.WINDOW_P_UPLIMIT = WINDOW_P_UPLIMIT 25 | self.WINDOW_P_STRUCT_UPLIMIT = WINDOW_P_STRUCT_UPLIMIT 26 | self.CODING_FREQUENCY = CODING_FREQUENCY 27 | self.VECTOR_REPETITION_CNN = VECTOR_REPETITION_CNN 28 | 29 | self.TRUNCATION_LEN = TRUNCATION_LEN 30 | self.PERIOD_EXTENDED = PERIOD_EXTENDED 31 | 32 | # list and position map for k_mer 33 | k_mers = [''] 34 | self.k_mer_list = [] 35 | self.k_mer_map = {} 36 | for T in range(self.WINDOW_P_UPLIMIT): 37 | temp_list = [] 38 | for k_mer in k_mers: 39 | for x in self.elements: 40 | temp_list.append(k_mer + x) 41 | k_mers = temp_list 42 | self.k_mer_list += temp_list 43 | for i in range(len(self.k_mer_list)): 44 | self.k_mer_map[self.k_mer_list[i]] = i 45 | 46 | # list and position map for k_mer structure 47 | k_mers = [''] 48 | self.k_mer_struct_list = [] 49 | self.k_mer_struct_map = {} 50 | for T in range(self.WINDOW_P_STRUCT_UPLIMIT): 51 | temp_list = [] 52 | for k_mer in k_mers: 53 | for s in self.structs: 54 | temp_list.append(k_mer + s) 55 | k_mers = temp_list 56 | self.k_mer_struct_list += temp_list 57 | for i in range(len(self.k_mer_struct_list)): 58 | self.k_mer_struct_map[self.k_mer_struct_list[i]] = i 59 | 60 | # table for amino acid clusters 61 | self.transtable = str.maketrans(self.pro_intab, self.pro_outtab) 62 | 63 | 64 | # print(len(self.k_mer_list)) 65 | # print(self.k_mer_list) 66 | # print(len(self.k_mer_struct_list)) 67 | # print(self.k_mer_struct_list) 68 | 69 | def encode_conjoint_previous(self, seq): 70 | seq = seq.translate(self.transtable) 71 | seq = ''.join([x for x in seq if x in self.elements]) 72 | seq_len = len(seq) 73 | if seq_len == 0: 74 | return 'Error' 75 | result = [] 76 | K = self.WINDOW_P_UPLIMIT 77 | offset = reduce(lambda x, y: x + y, map(lambda x: self.element_number ** x, range(1, K))) 78 | vec = [0.0] * (self.element_number ** K) 79 | counter = seq_len - K + 1 80 | for i in range(seq_len - K + 1): 81 | k_mer = seq[i:i + K] 82 | vec[self.k_mer_map[k_mer] - offset] += 1 83 | vec = np.array(vec) 84 | if self.CODING_FREQUENCY: 85 | vec = vec / vec.max() 86 | result += list(vec) 87 | return np.array(result) 88 | 89 | def encode_conjoint(self, seq): 90 | seq = seq.translate(self.transtable) 91 | seq = ''.join([x for x in seq if x in self.elements]) 92 | seq_len = len(seq) 93 | if seq_len == 0: 94 | return 'Error' 95 | result = [] 96 | offset = 0 97 | for K in range(1, self.WINDOW_P_UPLIMIT + 1): 98 | vec = [0.0] * (self.element_number ** K) 99 | counter = seq_len - K + 1 100 | for i in range(seq_len - K + 1): 101 | k_mer = seq[i:i + K] 102 | vec[self.k_mer_map[k_mer] - offset] += 1 103 | vec = np.array(vec) 104 | offset += vec.size 105 | if self.CODING_FREQUENCY: 106 | vec = vec / vec.max() 107 | result += list(vec) 108 | return np.array(result) 109 | 110 | def encode_conjoint_struct(self, seq, struct): 111 | seq = seq.translate(self.transtable) 112 | seq_temp = [] 113 | struct_temp = [] 114 | for i in range(len(seq)): 115 | if seq[i] in self.elements: 116 | seq_temp.append(seq[i]) 117 | struct_temp.append(struct[i]) 118 | seq = ''.join(seq_temp) 119 | struct = ''.join(struct_temp) 120 | seq_len = len(seq) 121 | if seq_len == 0: 122 | return 'Error' 123 | 124 | result_seq = [] 125 | offset_seq = 0 126 | for K in range(1, self.WINDOW_P_UPLIMIT + 1): 127 | vec_seq = [0.0] * (self.element_number ** K) 128 | counter = seq_len - K + 1 129 | for i in range(seq_len - K + 1): 130 | k_mer = seq[i:i + K] 131 | vec_seq[self.k_mer_map[k_mer] - offset_seq] += 1 132 | vec_seq = np.array(vec_seq) 133 | offset_seq += vec_seq.size 134 | if self.CODING_FREQUENCY: 135 | vec_seq = vec_seq / vec_seq.max() 136 | result_seq += list(vec_seq) 137 | 138 | 139 | result_struct = [] 140 | offset_struct = 0 141 | for K in range(1, self.WINDOW_P_STRUCT_UPLIMIT + 1): 142 | vec_struct = [0.0] * (self.struct_kind ** K) 143 | counter = seq_len - K + 1 144 | for i in range(seq_len - K + 1): 145 | k_mer_struct = struct[i:i + K] 146 | vec_struct[self.k_mer_struct_map[k_mer_struct] - offset_struct] += 1 147 | vec_struct = np.array(vec_struct) 148 | offset_struct += vec_struct.size 149 | if self.CODING_FREQUENCY: 150 | vec_struct = vec_struct / vec_struct.max() 151 | result_struct += list(vec_struct) 152 | return np.array(result_seq + result_struct) 153 | 154 | def encode_conjoint_cnn(self, seq): 155 | result_t = self.encode_conjoint(seq) 156 | result = np.array([[x] * self.VECTOR_REPETITION_CNN for x in result_t]) 157 | return result 158 | 159 | def encode_conjoint_struct_cnn(self, seq, struct): 160 | result_t = self.encode_conjoint_struct(seq, struct) 161 | result = np.array([[x] * self.VECTOR_REPETITION_CNN for x in result_t]) 162 | return result 163 | 164 | def encode_onehot(self, seq): 165 | seq = seq.translate(self.transtable) 166 | seq = ''.join([x for x in seq if x in self.elements]) 167 | seq_len = len(seq) 168 | if seq_len == 0: 169 | return 'Error' 170 | coding_len = min(len(seq), self.TRUNCATION_LEN) 171 | vec = [0] * coding_len 172 | for i in range(coding_len): 173 | pos = self.elements.index(seq[i]) 174 | vec[i] = [0] * self.element_number 175 | vec[i][pos] = 1 176 | if coding_len < self.TRUNCATION_LEN: 177 | gap_len = (self.TRUNCATION_LEN - coding_len) 178 | if self.PERIOD_EXTENDED: 179 | gap_term = int(math.ceil(float(gap_len) / coding_len)) 180 | gap_data = vec * gap_term 181 | vec += gap_data[0:gap_len] 182 | else: 183 | vec += [[0] * self.element_number] * gap_len 184 | return np.array(vec) 185 | 186 | def encode_onehot_struct(self, seq, struct): 187 | seq = seq.translate(self.transtable) 188 | seq_temp = [] 189 | struct_temp = [] 190 | for i in range(len(seq)): 191 | if seq[i] in self.elements: 192 | seq_temp.append(seq[i]) 193 | struct_temp.append(struct[i]) 194 | seq = ''.join(seq_temp) 195 | struct = ''.join(struct_temp) 196 | seq_len = len(seq) 197 | if seq_len == 0: 198 | return 'Error' 199 | coding_len = min(len(seq), self.TRUNCATION_LEN) 200 | vec = [0] * coding_len 201 | for i in range(coding_len): 202 | pos = self.elements.index(seq[i]) 203 | pos = pos + self.element_number * self.structs.index(struct[i]) 204 | vec[i] = [0] * (self.element_number * self.struct_kind) 205 | vec[i][pos] = 1 206 | if coding_len < self.TRUNCATION_LEN: 207 | gap_len = (self.TRUNCATION_LEN - coding_len) 208 | if self.PERIOD_EXTENDED: 209 | gap_term = int(math.ceil(float(gap_len) / coding_len)) 210 | gap_data = vec * gap_term 211 | vec += gap_data[0:gap_len] 212 | else: 213 | vec += [[0] * self.element_number * self.struct_kind] * gap_len 214 | return np.array(vec) 215 | 216 | def encode_word2vec(self, seq, pro_word2vec, window_size, stride): 217 | seq = seq.translate(self.transtable) 218 | seq_len = len(seq) 219 | if seq_len == 0: 220 | return 'Error' 221 | words = pro_word2vec.keys() 222 | VEC_LEN_W2V = len(list(words)[0]) 223 | MAX_PRO_W2V_LEN = self.TRUNCATION_LEN // stride 224 | vec = [] 225 | p = 0 226 | while (len(vec) < MAX_PRO_W2V_LEN) and (p + window_size <= seq_len): 227 | word = seq[p:p + window_size] 228 | if word in words: 229 | vec.append(pro_word2vec[word]) 230 | p += stride 231 | encoded_len = len(vec) 232 | if encoded_len == 0: 233 | return 'Error' 234 | elif encoded_len < MAX_PRO_W2V_LEN: 235 | gap_len = (MAX_PRO_W2V_LEN - encoded_len) 236 | if self.PERIOD_EXTENDED: 237 | gap_term = int(math.ceil(float(gap_len) / encoded_len)) 238 | gap_data = vec * gap_term 239 | vec += gap_data[0:gap_len] 240 | else: 241 | vec += [[0.0] * VEC_LEN_W2V] * gap_len 242 | return np.array(vec) 243 | 244 | def encode_word2vec_struct(self, seq, struct, pro_word2vec, window_size, stride): 245 | seq = seq.translate(self.transtable) 246 | seq_temp = [] 247 | struct_temp = [] 248 | for i in range(len(seq)): 249 | if seq[i] in self.elements: 250 | seq_temp.append(seq[i]) 251 | struct_temp.append(struct[i]) 252 | seq = ''.join(seq_temp) 253 | struct = ''.join(struct_temp) 254 | seq_len = len(seq) 255 | if seq_len == 0: 256 | return 'Error' 257 | 258 | words = pro_word2vec.keys() 259 | VEC_LEN_W2V = len(list(words)[0]) 260 | MAX_PRO_W2V_LEN = self.TRUNCATION_LEN // stride 261 | vec = [] 262 | p = 0 263 | while (len(vec) < MAX_PRO_W2V_LEN) and (p + window_size <= seq_len): 264 | word = seq[p:p + window_size] + struct[p:p + window_size] 265 | if word in words: 266 | vec.append(pro_word2vec[word]) 267 | p += stride 268 | encoded_len = len(vec) 269 | if encoded_len == 0: 270 | return 'Error' 271 | elif encoded_len < MAX_PRO_W2V_LEN: 272 | gap_len = (MAX_PRO_W2V_LEN - encoded_len) 273 | if self.PERIOD_EXTENDED: 274 | gap_term = int(math.ceil(float(gap_len) / encoded_len)) 275 | gap_data = vec * gap_term 276 | vec += gap_data[0:gap_len] 277 | else: 278 | vec += [[0.0] * VEC_LEN_W2V] * gap_len 279 | return np.array(vec) 280 | 281 | 282 | # encoder for RNA sequence 283 | class RNAEncoder: 284 | elements = 'AUCG' 285 | structs = '.(' 286 | 287 | element_number = 4 288 | struct_kind = 2 289 | 290 | def __init__(self, WINDOW_R_UPLIMIT, WINDOW_R_STRUCT_UPLIMIT, CODING_FREQUENCY, VECTOR_REPETITION_CNN, 291 | TRUNCATION_LEN=None, PERIOD_EXTENDED=None): 292 | 293 | self.WINDOW_R_UPLIMIT = WINDOW_R_UPLIMIT 294 | self.WINDOW_R_STRUCT_UPLIMIT = WINDOW_R_STRUCT_UPLIMIT 295 | self.CODING_FREQUENCY = CODING_FREQUENCY 296 | self.VECTOR_REPETITION_CNN = VECTOR_REPETITION_CNN 297 | 298 | self.TRUNCATION_LEN = TRUNCATION_LEN 299 | self.PERIOD_EXTENDED = PERIOD_EXTENDED 300 | 301 | # list and position map for k_mer 302 | k_mers = [''] 303 | self.k_mer_list = [] 304 | self.k_mer_map = {} 305 | for T in range(self.WINDOW_R_UPLIMIT): 306 | temp_list = [] 307 | for k_mer in k_mers: 308 | for x in self.elements: 309 | temp_list.append(k_mer + x) 310 | k_mers = temp_list 311 | self.k_mer_list += temp_list 312 | for i in range(len(self.k_mer_list)): 313 | self.k_mer_map[self.k_mer_list[i]] = i 314 | 315 | # list and position map for k_mer structure 316 | k_mers = [''] 317 | self.k_mer_struct_list = [] 318 | self.k_mer_struct_map = {} 319 | for T in range(self.WINDOW_R_STRUCT_UPLIMIT): 320 | temp_list = [] 321 | for k_mer in k_mers: 322 | for s in self.structs: 323 | temp_list.append(k_mer + s) 324 | k_mers = temp_list 325 | self.k_mer_struct_list += temp_list 326 | for i in range(len(self.k_mer_struct_list)): 327 | self.k_mer_struct_map[self.k_mer_struct_list[i]] = i 328 | 329 | # print(len(self.k_mer_list)) 330 | # print(self.k_mer_list) 331 | # print(len(self.k_mer_struct_list)) 332 | # print(self.k_mer_struct_list) 333 | 334 | def encode_conjoint_previous(self, seq): 335 | seq = seq.replace('T', 'U') 336 | seq = ''.join([x for x in seq if x in self.elements]) 337 | seq_len = len(seq) 338 | if seq_len == 0: 339 | return 'Error' 340 | result = [] 341 | K = self.WINDOW_R_UPLIMIT 342 | offset = reduce(lambda x, y: x + y, map(lambda x: self.element_number ** x, range(1, K))) 343 | vec = [0.0] * (self.element_number ** K) 344 | counter = seq_len - K + 1 345 | for i in range(seq_len - K + 1): 346 | k_mer = seq[i:i + K] 347 | vec[self.k_mer_map[k_mer] - offset] += 1 348 | vec = np.array(vec) 349 | if self.CODING_FREQUENCY: 350 | vec = vec / vec.max() 351 | result += list(vec) 352 | return np.array(result) 353 | 354 | def encode_conjoint(self, seq): 355 | seq = seq.replace('T', 'U') 356 | seq = ''.join([x for x in seq if x in self.elements]) 357 | seq_len = len(seq) 358 | if seq_len == 0: 359 | return 'Error' 360 | result = [] 361 | offset = 0 362 | for K in range(1, self.WINDOW_R_UPLIMIT + 1): 363 | vec = [0.0] * (self.element_number ** K) 364 | counter = seq_len - K + 1 365 | for i in range(seq_len - K + 1): 366 | k_mer = seq[i:i + K] 367 | vec[self.k_mer_map[k_mer] - offset] += 1 368 | vec = np.array(vec) 369 | offset += vec.size 370 | if self.CODING_FREQUENCY: 371 | vec = vec / vec.max() 372 | result += list(vec) 373 | return np.array(result) 374 | 375 | def encode_conjoint_struct(self, seq, struct): 376 | seq = seq.replace('T', 'U') 377 | struct = struct.replace(')', '(') 378 | seq_temp = [] 379 | struct_temp = [] 380 | for i in range(len(seq)): 381 | if seq[i] in self.elements: 382 | seq_temp.append(seq[i]) 383 | struct_temp.append(struct[i]) 384 | seq = ''.join(seq_temp) 385 | struct = ''.join(struct_temp) 386 | seq_len = len(seq) 387 | if seq_len == 0: 388 | return 'Error' 389 | 390 | result_seq = [] 391 | offset_seq = 0 392 | for K in range(1, self.WINDOW_R_UPLIMIT + 1): 393 | vec_seq = [0.0] * (self.element_number ** K) 394 | counter = seq_len - K + 1 395 | for i in range(seq_len - K + 1): 396 | k_mer = seq[i:i + K] 397 | vec_seq[self.k_mer_map[k_mer] - offset_seq] += 1 398 | vec_seq = np.array(vec_seq) 399 | offset_seq += vec_seq.size 400 | if self.CODING_FREQUENCY: 401 | vec_seq = vec_seq / vec_seq.max() 402 | result_seq += list(vec_seq) 403 | 404 | 405 | result_struct = [] 406 | offset_struct = 0 407 | for K in range(1, self.WINDOW_R_STRUCT_UPLIMIT + 1): 408 | vec_struct = [0.0] * (self.struct_kind ** K) 409 | counter = seq_len - K + 1 410 | for i in range(seq_len - K + 1): 411 | k_mer_struct = struct[i:i + K] 412 | vec_struct[self.k_mer_struct_map[k_mer_struct] - offset_struct] += 1 413 | vec_struct = np.array(vec_struct) 414 | offset_struct += vec_struct.size 415 | if self.CODING_FREQUENCY: 416 | vec_struct = vec_struct / vec_struct.max() 417 | result_struct += list(vec_struct) 418 | return np.array(result_seq + result_struct) 419 | 420 | def encode_conjoint_cnn(self, seq): 421 | result_t = self.encode_conjoint(seq) 422 | result = np.array([[x] * self.VECTOR_REPETITION_CNN for x in result_t]) 423 | return result 424 | 425 | def encode_conjoint_struct_cnn(self, seq, struct): 426 | result_t = self.encode_conjoint_struct(seq, struct) 427 | result = np.array([[x] * self.VECTOR_REPETITION_CNN for x in result_t]) 428 | return result 429 | 430 | def encode_onehot(self, seq): 431 | seq = seq.replace('T', 'U') 432 | seq = ''.join([x for x in seq if x in self.elements]) 433 | seq_len = len(seq) 434 | if seq_len == 0: 435 | return 'Error' 436 | coding_len = min(len(seq), self.TRUNCATION_LEN) 437 | vec = [0] * coding_len 438 | for i in range(coding_len): 439 | pos = self.elements.index(seq[i]) 440 | vec[i] = [0] * self.element_number 441 | vec[i][pos] = 1 442 | if coding_len < self.TRUNCATION_LEN: 443 | gap_len = (self.TRUNCATION_LEN - coding_len) 444 | if self.PERIOD_EXTENDED: 445 | gap_term = int(math.ceil(float(gap_len) / coding_len)) 446 | gap_data = vec * gap_term 447 | vec += gap_data[0:gap_len] 448 | else: 449 | vec += [[0] * self.element_number] * gap_len 450 | return np.array(vec) 451 | 452 | def encode_onehot_struct(self, seq, struct): 453 | seq = seq.replace('T', 'U') 454 | struct = struct.replace(')', '(') 455 | seq_temp = [] 456 | struct_temp = [] 457 | for i in range(len(seq)): 458 | if seq[i] in self.elements: 459 | seq_temp.append(seq[i]) 460 | struct_temp.append(struct[i]) 461 | seq = ''.join(seq_temp) 462 | struct = ''.join(struct_temp) 463 | seq_len = len(seq) 464 | if seq_len == 0: 465 | return 'Error' 466 | coding_len = min(len(seq), self.TRUNCATION_LEN) 467 | vec = [0] * coding_len 468 | for i in range(coding_len): 469 | pos = self.elements.index(seq[i]) 470 | pos = pos + self.element_number * self.structs.index(struct[i]) 471 | vec[i] = [0] * (self.element_number * self.struct_kind) 472 | vec[i][pos] = 1 473 | if coding_len < self.TRUNCATION_LEN: 474 | gap_len = (self.TRUNCATION_LEN - coding_len) 475 | if self.PERIOD_EXTENDED: 476 | gap_term = int(math.ceil(float(gap_len) / coding_len)) 477 | gap_data = vec * gap_term 478 | vec += gap_data[0:gap_len] 479 | else: 480 | vec += [[0] * self.element_number * self.struct_kind] * gap_len 481 | return np.array(vec) 482 | 483 | def encode_word2vec(self, seq, rna_word2vec, window_size, stride): 484 | seq = seq.replace('T', 'U') 485 | seq = ''.join([x for x in seq if x in self.elements]) 486 | seq_len = len(seq) 487 | if seq_len == 0: 488 | return 'Error' 489 | words = rna_word2vec.keys() 490 | VEC_LEN_W2V = len(list(words)[0]) 491 | MAX_RNA_W2V_LEN = self.TRUNCATION_LEN // stride 492 | vec = [] 493 | p = 0 494 | while (len(vec) < MAX_RNA_W2V_LEN) and (p + window_size <= seq_len): 495 | word = seq[p:p + window_size] 496 | if word in words: 497 | vec.append(rna_word2vec[word]) 498 | p += stride 499 | encoded_len = len(vec) 500 | if encoded_len == 0: 501 | return 'Error' 502 | elif encoded_len < MAX_RNA_W2V_LEN: 503 | gap_len = (MAX_RNA_W2V_LEN - encoded_len) 504 | if self.PERIOD_EXTENDED: 505 | gap_term = int(math.ceil(float(gap_len) / encoded_len)) 506 | gap_data = vec * gap_term 507 | vec += gap_data[0:gap_len] 508 | else: 509 | vec += [[0.0] * VEC_LEN_W2V] * gap_len 510 | return np.array(vec) 511 | 512 | def encode_word2vec_struct(self, seq, struct, rna_word2vec, window_size, stride): 513 | seq = seq.replace('T', 'U') 514 | struct = struct.replace(')', '(') 515 | seq_temp = [] 516 | struct_temp = [] 517 | for i in range(len(seq)): 518 | if seq[i] in self.elements: 519 | seq_temp.append(seq[i]) 520 | struct_temp.append(struct[i]) 521 | seq = ''.join(seq_temp) 522 | struct = ''.join(struct_temp) 523 | seq_len = len(seq) 524 | if seq_len == 0: 525 | return 'Error' 526 | 527 | words = rna_word2vec.keys() 528 | VEC_LEN_W2V = len(list(words)[0]) 529 | MAX_RNA_W2V_LEN = self.TRUNCATION_LEN // stride 530 | vec = [] 531 | p = 0 532 | while (len(vec) < MAX_RNA_W2V_LEN) and (p + window_size <= seq_len): 533 | word = seq[p:p + window_size] + struct[p:p + window_size] 534 | if word in words: 535 | vec.append(rna_word2vec[word]) 536 | p += stride 537 | encoded_len = len(vec) 538 | if encoded_len == 0: 539 | return 'Error' 540 | elif encoded_len < MAX_RNA_W2V_LEN: 541 | gap_len = (MAX_RNA_W2V_LEN - encoded_len) 542 | if self.PERIOD_EXTENDED: 543 | gap_term = int(math.ceil(float(gap_len) / encoded_len)) 544 | gap_data = vec * gap_term 545 | vec += gap_data[0:gap_len] 546 | else: 547 | vec += [[0.0] * VEC_LEN_W2V] * gap_len 548 | return np.array(vec) 549 | --------------------------------------------------------------------------------