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Front. Genet. | doi: 10.3389/fgene.2019.01205

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

  • 1Department of Computer Science and Technology, University of Cambridge, United Kingdom
  • 2Department of Biological Engineering, Massachusetts Institute of Technology, United States

International initiatives such as the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) are collecting multiple data sets at different genome-scales with the aim to identify novel cancer bio-markers and predict patient survival. To analyse 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 analyse 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.

Keywords: machine learning, Cancer - Breast cancer, Variational autoencoder, Deep Learinng, Integrative data analyses (IDA), Artificial inteleigence, Bioinformactics, multi-omic analysis

Received: 29 Jul 2019; Accepted: 31 Oct 2019.

Copyright: © 2019 Simidjievski, Bodnar, Tariq, Scherer, Andres Terre, Shams, Jamnik and Lio'. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Nikola Simidjievski, University of Cambridge, Department of Computer Science and Technology, Cambridge, United Kingdom,