Genomic and algorithm-based predictive risk assessment models for benzene exposure.

ABSTRACT:

Aim

In this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.

Subject and methods

We sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. Utilizing R software, we performed an initial screen for differentially expressed genes (DEGs), which was followed by the enrichment analyses to elucidate the affected functions and pathways. Subsequent steps included the application of three machine learning algorithms for key gene identification, and the validation of these genes within both a cohort exposed to benzene and a benzene-exposed mice model. We then conducted a functional prediction analysis

SUBMITTER: Jiang M 

PROVIDER: S-EPMC11795664 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature