A new framework for drug–disease association prediction combing light-gated message passing neural network and gated fusion mechanism. Issue 6 (27th October 2022)

• Record Type: Journal Article
• Authors: Liu, Bao-Min; Gao, Ying-Lian; Zhang, Dai-Jun; Zhou, Feng; Wang, Juan; Zheng, Chun-Hou; Liu, Jin-Xing
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-10-27
• Subjects: drug–disease association prediction -- neural network -- message passing -- forget gate -- output gate; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac457
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational methods is indispensable for finding new candidate diseases to treat with existing drugs. In this paper, a model integrating a new variant of message passing neural network and a novel-gated fusion mechanism called GLGMPNN is proposed for drug–disease association prediction. First, a light-gated message passing neural network (LGMPNN), including message passing, aggregation and updating, is proposed to separately extract multiple pieces of information from the similarity networks and the association network. Then, a gated fusion mechanism consisting of a forget gate and an output gate is applied to integrate the multiple pieces of information to extent. The forget gate calculated by the multiple embeddings is built to integrate the association information into the similarity information. Furthermore, the final node representations are controlled by the output gate, which fuses the topology information of the networks and the initial similarity information. Finally, a bilinear decoder is adopted to reconstruct an adjacency matrix for drug–disease associations. Evaluated by 10-fold cross-validations, GLGMPNN achieves excellent performance compared with the current models. The following studies show that our model can effectively discover novel drug–disease associations.