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 almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx an

SUBMITTER: Smith AM 

PROVIDER: S-EPMC7085143 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature