A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.

ABSTRACT: Complex high-dimensional datasets that are challenging to analyze are frequently produced through '-omics' profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in '-omics' data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in '-omics' data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected gene

SUBMITTER: Gerolami J 

PROVIDER: S-EPMC9407361 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature