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

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...

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Detalles Bibliográficos
Autores principales: Smith, Aaron M., Walsh, Jonathan R., Long, John, Davis, Craig B., Henstock, Peter, Hodge, Martin R., Maciejewski, Mateusz, Mu, Xinmeng Jasmine, Ra, Stephen, Zhao, Shanrong, Ziemek, Daniel, Fisher, Charles K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085143/
https://www.ncbi.nlm.nih.gov/pubmed/32197580
http://dx.doi.org/10.1186/s12859-020-3427-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085143/
https://www.ncbi.nlm.nih.gov/pubmed/32197580
http://dx.doi.org/10.1186/s12859-020-3427-8

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