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...

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Bibliographic Details
Main Author:	Jinhui Zhao (1723234) (author)
Other Authors:	Jiarui He (283042) (author), Pengwei Guan (11698746) (author), Han Bao (1905496) (author), Xinjie Zhao (239844) (author), Chunxia Zhao (305710) (author), Wangshu Qin (9629390) (author), Xin Lu (24821) (author), Guowang Xu (135666) (author)
Published:	2025
Subjects:	Medicine Genetics Biotechnology Sociology Cancer Biological Sciences not elsewhere classified Information Systems not elsewhere classified variational graph autoencoder systems biology research systems biology perspective specific functional molecules offering new insights meaningfully interpret complex functionally coherent modules facilitating discovery even enhancing functional annotation current analytical approaches characteristic sparsity patterns graph convolutional networks explainable heterogeneous networks various data types related data sets inherent data heterogeneity precision medicine applications persistent interpretability gap https :// github omics data sets investigate disease mechanisms omics data regulatory networks multiomics data incomplete data translational medicine github repository critical gap useful approach unprecedented resolution source code rapid growth publicly available particularly valuable often missed multiomics integration multiomics context mining disease major challenges identifying disease gcn ). framework ’ effectively addressing critical limitations conventional methods conditions makes computational analysis challenging scenarios biological understanding associated biomarkers
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MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data

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