  Multi-omic data provides multiple views of the same patients. Integrative
analysis of multi-omic data is crucial to elucidate the molecular underpinning
of disease etiology. However, multi-omic data has the "big p, small N" problem
(the number of features is large, but the number of samples is small), it is
challenging to train a complicated machine learning model from the multi-omic
data alone and make it generalize well. Here we propose a framework termed
Multi-view Factorization AutoEncoder with network constraints to integrate
multi-omic data with domain knowledge (biological interactions networks). Our
framework employs deep representation learning to learn feature embeddings and
patient embeddings simultaneously, enabling us to integrate feature interaction
network and patient view similarity network constraints into the training
objective. The whole framework is end-to-end differentiable. We applied our
approach to the TCGA Pan-cancer dataset and achieved satisfactory results to
predict disease progression-free interval (PFI) and patient overall survival
(OS) events. Code will be made publicly available.
