Machine Learning-Based Analysis of Glioma Grades Reveals Co-Enrichment.

ABSTRACT: Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We fur

SUBMITTER: Garbulowski M 

PROVIDER: S-EPMC8870250 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature