Predictive genetic panel for adult asthma using machine learning methods.

ABSTRACT:

Background

Asthma is a chronic inflammatory disease of the airways that is heterogeneous and multifactorial, making its accurate characterization a complex process. Therefore, identifying the genetic variations associated with asthma and discovering the molecular interactions between the omics that confer risk of developing this disease will help us to unravel the biological pathways involved in its pathogenesis.

Objective

We sought to develop a predictive genetic panel for asthma using machine learning methods.

Methods

We tested 3 variable selection methods: Boruta's algorithm, the top 200 genome-wide association study markers according to their respective P values, and an elastic net regression. Ten different algorithms were chosen for the classification test

SUBMITTER: Gomes LGDS 

PROVIDER: S-EPMC11215340 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature