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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
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 |
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author | 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. |
author_facet | 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. |
author_sort | Smith, Aaron M. |
collection | PubMed |
description | 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 and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l(2)-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge. |
format | Online Article Text |
id | pubmed-7085143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70851432020-03-23 Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data 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. BMC Bioinformatics Research Article 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 and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l(2)-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge. BioMed Central 2020-03-20 /pmc/articles/PMC7085143/ /pubmed/32197580 http://dx.doi.org/10.1186/s12859-020-3427-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article 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. Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title_full | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title_fullStr | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title_full_unstemmed | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title_short | Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
title_sort | standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data |
topic | Research Article |
url | 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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