Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Journal Articles Frontiers in Genetics Year : 2019

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

Abstract

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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hal-02623625 , version 1 (26-05-2020)

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Mickael Leclercq, Benjamin Vittrant, Marie-Laure Magniette, Marie Pier Scott Boyer, Olivier Perin, et al.. Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data. Frontiers in Genetics, 2019, 10, pp.452. ⟨10.3389/fgene.2019.00452⟩. ⟨hal-02623625⟩
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