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

Clicks: 244
ID: 100052
2019
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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 data). We perform extensive analyses of these approaches and provide a clear methodological and computational framework for designing systems that enable clinicians to investigate cancer traits and translate the results into clinical applications. We demonstrate how these networks can be designed, built, and, in particular, applied to tasks of integrative analyses of heterogeneous breast cancer data. The results show that these approaches yield relevant data representations that, in turn, lead to accurate and stable diagnosis.
Reference Key
simidjievski2019variationalfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Simidjievski, Nikola;Bodnar, Cristian;Tariq, Ifrah;Scherer, Paul;Andres Terre, Helena;Shams, Zohreh;Jamnik, Mateja;Liò, Pietro;
Journal Frontiers in genetics
Year 2019
DOI
10.3389/fgene.2019.01205
URL
Keywords
Deep learning artificial intelligence Machine learning variational autoencoder bioinformactics cancer–breast cancer integrative data analyses multi-omic analysis

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