Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

ABSTRACT: Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be

SUBMITTER: Grissa D 

PROVIDER: S-EPMC4937038 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature