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

SUBMITTER: Leclercq M 

PROVIDER: S-EPMC6532608 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature