Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data

Chen, Yang, Goodison, Sun (2020) Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data Bioinformatics (IF: 5.8) 36(5) 1476-1483

Abstract

Cancer subtype classification has the potential to significantly improve disease prognosis and develop individualized patient management. Existing methods are limited by their ability to handle extremely high-dimensional data and by the influence of misleading, irrelevant factors, resulting in ambiguous and overlapping subtypes.To address the above issues, we proposed a novel approach to disentangling and eliminating irrelevant factors by leveraging the power of deep learning. Specifically, we designed a deep-learning framework, referred to as DeepType, that performs joint supervised classification, unsupervised clustering and dimensionality reduction to learn cancer-relevant data representation with cluster structure. We applied DeepType to the METABRIC breast cancer dataset and compared its performance to state-of-the-art methods. DeepType significantly outperformed the existing methods, identifying more robust subtypes while using fewer genes. The new approach provides a framework for the derivation of more accurate and robust molecular cancer subtypes by using increasingly complex, multi-source data.An open-source software package for the proposed method is freely available at http://www.acsu.buffalo.edu/~yijunsun/lab/DeepType.html.Supplementary data are available at Bioinformatics online.© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Links

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8215925
http://www.ncbi.nlm.nih.gov/pubmed/31603461
http://dx.doi.org/10.1093/bioinformatics/btz769

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