Frontiers in Genetics (May 2019)

Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data

  • Mickael Leclercq,
  • Mickael Leclercq,
  • Benjamin Vittrant,
  • Benjamin Vittrant,
  • Marie Laure Martin-Magniette,
  • Marie Laure Martin-Magniette,
  • Marie Pier Scott Boyer,
  • Marie Pier Scott Boyer,
  • Olivier Perin,
  • Alain Bergeron,
  • Alain Bergeron,
  • Yves Fradet,
  • Yves Fradet,
  • Arnaud Droit,
  • Arnaud Droit

DOI
https://doi.org/10.3389/fgene.2019.00452
Journal volume & issue
Vol. 10

Abstract

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The identification of biomarker signatures in omics molecular profiling is usually performed to predict outcomes in a precision medicine context, such as patient disease susceptibility, diagnosis, prognosis, and treatment response. To identify these signatures, we have developed a biomarker discovery tool, called BioDiscML. From a collection of samples and their associated characteristics, i.e., the biomarkers (e.g., gene expression, protein levels, clinico-pathological data), BioDiscML exploits various feature selection procedures to produce signatures associated to machine learning models that will predict efficiently a specified outcome. To this purpose, BioDiscML uses a large variety of machine learning algorithms to select the best combination of biomarkers for predicting categorical or continuous outcomes from highly unbalanced datasets. The software has been implemented to automate all machine learning steps, including data pre-processing, feature selection, model selection, and performance evaluation. BioDiscML is delivered as a stand-alone program and is available for download at https://github.com/mickaelleclercq/BioDiscML.

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