PIMO: pathway-based interpretable multiomics interactions for multiomics integration.

Abstract

Modeling interomics interactions across multiple molecular levels is critical for deciphering the mechanisms underlying complex diseases. Epigenomic and structural alterations, such as DNA methylation and copy number alterations (CNAs), modulate gene expression and collectively influence disease progression and patient survival outcomes. Despite advancements in deep learning-based multiomics analysis, gene-level interactions of interomics have been seldom considered, due to combinational complexity and power, which limits interpretability and mechanistic insight. We propose a pathway-based interpretable deep learning multiomics interaction model, PIMO, that explicitly captures regulatory effects across omics layers. Experiments on multiple TCGA cancer datasets showed that PIMO consistently outperformed state-of-the-art baselines in survival analysis, up to 13% increase in the C-index. PIMO provides biologically interpretable analyses that identify important pathways, genes, and interomics interactions with DNA methylation and CNAs. The source code and data are available at https://github.com/datax-lab/PIMO.