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Predicting CircRNA-Disease associations via feature convolution learning with heterogeneous graph attention network (JBHI'23)

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License

Copyright (C) 2022 Li Peng ([email protected]), Cheng Yang ([email protected])

This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program; if not, see http://www.gnu.org/licenses/.

GATCL2CD

GATCL2CD is effective to predict associations between circRNAs and diseases, which is based on feature convolution learning with heterogeneous graph attention network.

Environment Requirement

  • torch == 1.7.1+cu110
  • numpy == 1.19.5
  • matplotlib == 3.5.1
  • dgl-cu110 == 0.5.3

Model

  • GAT_layer_v2: Coding multi-head dynamic attention mechanism.
  • GATCL.py: the core model proposed in the paper.
  • fivefold_CV.py: completion of a 5-fold cross-validation experiment.
  • case_study.py: get scores for candidate circRNAs for all diseases.

Compare_models

  • There are five state-of-the-art models including: DMFCDA, CD_LNLP, RWR, GATCDA, IGNSCDA, which are compared under the same experiment settings.

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Predicting CircRNA-Disease associations via feature convolution learning with heterogeneous graph attention network (JBHI'23)

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