Deep learning of multimodal networks with topological regularization for drug repositioning.

ABSTRACT:

Motivation

Computational techniques for drug-disease prediction are essential in enhancing drug discovery and repositioning. While many methods utilize multimodal networks from various biological databases, few integrate comprehensive multi-omics data, including transcriptomes, proteomes, and metabolomes. We introduce STRGNN, a novel graph deep learning approach that predicts drug-disease relationships using extensive multimodal networks comprising proteins, RNAs, metabolites, and compounds. We have constructed a detailed dataset incorporating multi-omics data and developed a learning algorithm with topological regularization. This algorithm selectively leverages informative modalities while filtering out redundancies.

Results

STRGNN demonstrates superior accuracy compared t

SUBMITTER: Ohnuki Y 

PROVIDER: S-EPMC11342530 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature