Deep trans-omic network fusion reveals altered synaptic network in Alzheimer’s Disease

Abstract

Multi-omic data spanning from genotype, gene expression to protein expression have been increasingly explored, with attempt to better interpret genetic findings from genome wide association studies and to gain more insight of the disease mechanism. However, gene expression and protein expression are part of dynamic process changing in various ways as a cell ages. Expression data captured by existing technology is often noisy and only capture a screenshot of the dynamic process. Performance of models built on top of these expression data is undoubtedly compromised. To address this problem, we propose a new interpretable deep multi-omic network fusion model (MoFNet) for predictive modeling of Alzheimer′s disease. In particular, the information flow from DNA to protein is leveraged as a prior multi- omic network to enhance the signal in gene and protein expression data so as to achieve better prediction power. The proposed model MoFNet significantly outperformed all other state-of-art classifiers when evaluated using genotype, gene expression and protein expression data from the ROS/MAP cohort. Instead of individual markers, MoFNet yielded 3 major multi-omic subnetworks related to innate immune system, clearance of unwanted cells or misfolded proteins, and neurotransmitter release respectively.