Identifying Subgroups of Patients With Autism by Gene Expression Profiles Using Machine Learning Algorithms.

ABSTRACT: Objectives: The identification of subgroups of autism spectrum disorder (ASD) may partially remedy the problems of clinical heterogeneity to facilitate the improvement of clinical management. The current study aims to use machine learning algorithms to analyze microarray data to identify clusters with relatively homogeneous clinical features. Methods: The whole-genome gene expression microarray data were used to predict communication quotient (SCQ) scores against all probes to select differential expression regions (DERs). Gene set enrichment analysis was performed for DERs with a fold-change >2 to identify hub pathways that play a role in the severity of social communication deficits inherent to ASD. We then used two machine learning methods, random forest classification (RF

SUBMITTER: Lin PI 

PROVIDER: S-EPMC8149626 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature