From Gene Networks to Therapeutics: A Causal Inference and Deep Learning Approach for Drug Discovery.

ABSTRACT: Background/Objectives: Drug discovery is a lengthy and expensive process, taking an average of 10 years and more than USD 2 billion from target discovery to drug approval. It is even more challenging in complex diseases due to disease heterogeneity and limited knowledge about the underlying mechanisms. We present a novel computational framework that integrates network analysis, statistical mediation, and deep learning to identify causal target genes and repurposable small-molecule candidates. Methods: We applied weighted gene co-expression network analysis (WGCNA) and bidirectional mediation analysis (causal WGCNA) to transcriptomic data from idiopathic pulmonary fibrosis (IPF) patients to identify genes causally linked to the disease phenotype. These genes were used as a phe

SUBMITTER: Ghandikota S 

PROVIDER: S-EPMC12472719 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature