Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)

摘要

With the fast adoption of Next Generation Sequencing (NGS) technologies, petabytes of genomic, transcriptomic, proteomic, and epigenomic data (collectively called multi-omics data) have been accumulated in the past decade. Notably, The Cancer Genome Atlas (TCGA) Network [ ] alone had generated over one petabyte of multi-omics data for comprehensive molecular profiling of over 11,000 patients from 33 cancer types. Multi-omics data includes multiple types of -omics data, each of which represents one view and has a different feature set (for instance, gene expressions, miRNA expressions, and so on). Since multiple views for the same patients can provide complementary information, integrative analysis of multi-omics data with machine learning approaches has great potentials to elucidate the molecular underpinning of disease etiology. However, due to the “big p, small n” problem, many statistical machine learning approaches that require lots of training data may fail to extract true signals from multi-omics data alone.