Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer’s disease

• Published in: Scientific reports Vol. 15; no. 1; pp. 30182 - 11
• Main Authors: Xie, Linhui, Raj, Yash, Tong, Mingzhao, Nho, Kwangsik, Salama, Paul, Saykin, Andrew J., Fang, Shiaofen, Yan, Jingwen
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.08.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/553; Alzheimer's disease; Biological analysis; Blood-brain barrier; Classification; Cortex (frontal); Deep learning; Disease; Gene mapping; Genes; Genetic diversity; Genetic variance; Genomics; Humanities and Social Sciences; Kinases; Missing data; Molecular modelling; Multi-omic network; multidisciplinary; Neural networks; Neurodegenerative diseases; Peptides; Permeability; Phosphorylation; Prediction models; Proteins; Quantitative trait loci; Regression analysis; Science; Science (multidisciplinary); Synaptogenesis; Systems biology; Transfer learning; Vascular endothelial growth factor; Deep learning; Alzheimer’s Disease; Transfer learning; Systems biology; Multi-omic network
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-14636-2

Multi-omics data provides a comprehensive view of biological systems and enables researchers to uncover intricate molecular mechanisms underlying complex diseases. However, multi-omic data is often incomplete and joint modeling of multi-omics data will lead to exclusion of a large portion of subjects. Furthermore, most current multi-omics studies pinpoint individual -omics markers, which may not interact, posing challenges for interpretation. In this study, we developed an interpretable deep trans-omic fusion neural network, TransFuse, to include incomplete -omic data for training of prediction models. When evaluated using the data from two Alzheimer’s disease cohorts, TransFuse generally showed superior or comparable performance over competing methods in a wide range of metrics like classification accuracy and F1 score. In addition, TransFuse yielded a subset of multi-omics features forming functional disease network modules, providing valuable insights into underlying molecular mechanism. In addition, almost all the genetic variants identified by TransFuse are expression quantitative trait locus (eQTLs) specific to frontal cortex tissue, from which the gene and protein expression data were collected. This highlights the great potential of TransFuse in capturing the tissue-specific information flow. Top pathways enriched include VEGF and EPH pathways, both influencing neural development and synaptic formation.