Large-Scale Automatic Feature Selection for Biomarker Discovery in High-Dimensional OMICs Data.
biomarkers signature
feature selection
machine learning
omics
precision medicine
Journal
Frontiers in genetics
ISSN: 1664-8021
Titre abrégé: Front Genet
Pays: Switzerland
ID NLM: 101560621
Informations de publication
Date de publication:
2019
2019
Historique:
received:
23
01
2019
accepted:
30
04
2019
entrez:
4
6
2019
pubmed:
4
6
2019
medline:
4
6
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 31156708
doi: 10.3389/fgene.2019.00452
pmc: PMC6532608
doi:
Types de publication
Journal Article
Langues
eng
Pagination
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