Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice.

ABSTRACT: International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyze such data, several machine learning, bioinformatics, and statistical methods have been applied, among them neural networks such as autoencoders. Although these models provide a good statistical learning framework to analyze multi-omic and/or clinical data, there is a distinct lack of work on how to integrate diverse patient data and identify the optimal design best suited to the available data.In this paper, we investigate several autoencoder architectures that integrate a variety of cancer patient data types (e.g., multi-omics and clinical da

SUBMITTER: Simidjievski N 

PROVIDER: S-EPMC6917668 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature