Modelamiento matemático sobre la expresión genética para el diagnóstico de sibilancias recurrentes asociadas a aeroalérgenos

Abstract

Gene expression in preschool children with and without wheezing was analyzed using public data from the NCBI (GSE261070) to identify differentially expressed genes. A supervised classification mathematical model using LASSO regression was applied to select genes with high predictive power and discriminate between individuals with and without wheezing. The model was evaluated in independent data sets, achieving ideal predictive performance. Subsequently, a differential expression analysis was performed to obtain significantly regulated genes and, based on these, a functional enrichment study (GO, KEGG, GSEA, and DOSE) was carried out to detect relevant metabolic pathways and biological processes. This study suggests that, in the future, the set of identified genes could form a useful diagnostic panel for the early detection of recurrent wheezing, contributing to the molecular understanding of this respiratory condition in children.