SNF–CVAE: Computational method to predict drug–disease interactions using similarity network fusion and collective variational autoencoder

作者：

Highlights：

• We propose a robust approach, SNF–CVAE, for predicting novel drug–disease interactions using drug-related similarity information and known drug–disease interactions.

• We evaluated the robustness of SNF–CVAE using different information models, drug similarity calculation measures, and drug similarity information.

• We compared SNF–CVAE performance with four state-of-the-art machine learning models. SNF–CVAE achieved outstanding performance in stratified 5-fold cross-validation.

• We conducted two case studies on the top predicted drug candidates for potentially treating Alzheimer’s disease and Juvenile rheumatoid arthritis, which were successfully validated against clinical trials and published studies. In conclusion,

• We strongly believe that computational drug repurposing research could significantly benefit from integrating similarity measures and deep learning models to predict novel drug–disease interactions in heterogeneous networks.

摘要

•We propose a robust approach, SNF–CVAE, for predicting novel drug–disease interactions using drug-related similarity information and known drug–disease interactions.•We evaluated the robustness of SNF–CVAE using different information models, drug similarity calculation measures, and drug similarity information.•We compared SNF–CVAE performance with four state-of-the-art machine learning models. SNF–CVAE achieved outstanding performance in stratified 5-fold cross-validation.•We conducted two case studies on the top predicted drug candidates for potentially treating Alzheimer’s disease and Juvenile rheumatoid arthritis, which were successfully validated against clinical trials and published studies. In conclusion,•We strongly believe that computational drug repurposing research could significantly benefit from integrating similarity measures and deep learning models to predict novel drug–disease interactions in heterogeneous networks.

论文关键词：Computational drug repositioning,Drug similarity measures,Similarity network fusion,Machine learning,Deep learning

论文评审过程：Received 9 June 2020, Revised 23 September 2020, Accepted 29 October 2020, Available online 1 November 2020, Version of Record 24 December 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.106585