MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data

The rapid growth of multiomics data has revolutionized our ability to investigate disease mechanisms, yet significant challenges persist in achieving meaningful integration due to inherent data heterogeneity, characteristic sparsity patterns, and the persistent interpretability gap in current analyt...

Full description

Saved in:
Bibliographic Details
Published in:Analytical chemistry (Washington) Vol. 97; no. 42; p. 23222
Main Authors: Zhao, Jinhui, He, Jiarui, Guan, Pengwei, Bao, Han, Zhao, Xinjie, Zhao, Chunxia, Qin, Wangshu, Lu, Xin, Xu, Guowang
Format: Journal Article
Language:English
Published: United States 28.10.2025
Subjects:
Autoencoder
Data Mining
Deep Learning
Humans
ISSN:1520-6882, 1520-6882
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The rapid growth of multiomics data has revolutionized our ability to investigate disease mechanisms, yet significant challenges persist in achieving meaningful integration due to inherent data heterogeneity, characteristic sparsity patterns, and the persistent interpretability gap in current analytical approaches. To address these critical limitations, we introduce MODAPro, a biologically informed deep learning framework that synergistically integrates variational graph autoencoders (VAE) with graph convolutional networks (GCN). This novel architecture enables MODAPro to capture and meaningfully interpret complex, nonlinear molecular relationships across different omics layers with unprecedented resolution. Through systematic benchmarking across diverse disease-related data sets, MODAPro consistently outperforms existing approaches in identifying disease-associated biomarkers and functionally coherent modules. Importantly, MODAPro reveals latent biomolecular information that is often missed by conventional methods. In realistic and challenging scenarios, MODAPro effectively captures intricate across-omic interactions, enhancing functional annotation and offering new insights into disease from a systems biology perspective, bridging a critical gap between computational analysis and biological understanding. Furthermore, MODAPro retains robust performance on single-omics data sets by leveraging the multiomics context, facilitating discovery even from sparse or incomplete data. The framework's adaptability to various data types and conditions makes it particularly valuable for precision medicine applications, where it can uncover actionable disease signatures and regulatory networks that would otherwise remain undetected. By effectively addressing the major challenges in multiomics integration, MODAPro provides a useful approach for systems biology research and translational medicine. The source code of MODAPro is publicly available in our GitHub repository: https://github.com/zhaoxiaoqi0714/MODAPro.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1520-6882
1520-6882
DOI:10.1021/acs.analchem.5c03611