Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.

ABSTRACT: This study proposes a novel machine learning (ML)-based stacking technique that integrates Single Nucleotide Polymorphisms (SNPs) and inferred local ancestry (LA) to improve predictive accuracy in clinical outcomes. Asthma, particularly severe asthma (SA) with poor response to inhaled corticosteroids (ICS), serves as the case study to illustrate this approach. Using data from the Biorepository and Integrative Genomics (BIG) Initiative, which includes whole-exome sequenced data from a self-reported African American pediatric cohort (N=248), we develop an ML framework to predict ICS response. After SNP data preprocessing and LA estimation, we employ stratified 10-fold cross-validation, creating base pipelines for SNP and LA data, which are then combined in stacked pipelines to assess the eff

SUBMITTER: Tahmin N 

PROVIDER: S-EPMC12623762 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature