Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data

Abstract Background The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in alm...

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Bibliographic Details
Main Authors: Aaron M. Smith, Jonathan R. Walsh, John Long, Craig B. Davis, Peter Henstock, Martin R. Hodge, Mateusz Maciejewski, Xinmeng Jasmine Mu, Stephen Ra, Shanrong Zhao, Daniel Ziemek, Charles K. Fisher
Format: Article
Language:English
Published: BMC 2020-03-01
Series:BMC Bioinformatics
Subjects:
Transcriptomics
RNA-seq
Normalization methods
Machine learning
Deep learning
Representation learning
Online Access:http://link.springer.com/article/10.1186/s12859-020-3427-8

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http://link.springer.com/article/10.1186/s12859-020-3427-8

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