Identification of Smoking-Associated Transcriptome Aberration in Blood with Machine Learning Methods.

ABSTRACT: Long-term cigarette smoking causes various human diseases, including respiratory disease, cancer, and gastrointestinal (GI) disorders. Alterations in gene expression and variable splicing processes induced by smoking are associated with the development of diseases. This study applied advanced machine learning methods to identify the isoforms with important roles in distinguishing smokers from former smokers based on the expression profile of isoforms from current and former smokers collected in one previous study. These isoforms were deemed as features, which were first analyzed by the Boruta to select features highly correlated with the target variables. Then, the selected features were evaluated by four feature ranking algorithms, resulting in four feature lists. The incremental feature

SUBMITTER: Huang F 

PROVIDER: S-EPMC9833906 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature