Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease.

ABSTRACT:

Background

Biomarker discovery exploiting feature importance of machine learning has risen recently in the microbiome landscape with its high predictive performance in several disease states. To have a concrete selection among a high number of features, recursive feature elimination (RFE) has been widely used in the bioinformatics field. However, machine learning-based RFE has factors that decrease the stability of feature selection. In this article, we suggested methods to improve stability while sustaining performance.

Results

We exploited the abundance matrices of the gut microbiome (283 taxa at species level and 220 at genus level) to classify between patients with inflammatory bowel disease (IBD) and healthy control (1,569 samples). We found that applying an already pub

SUBMITTER: Lee Y 

PROVIDER: S-EPMC10600917 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature