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

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) and support vector machine (SVM), to identify two clusters using DERs. Finally, we evaluated how accurately the clusters predicted language impairment. Results: A total of 191 DERs were initially identified, and 54 of them with a fold-change >2 were selected for the pathway analysis. Cholesterol biosynthesis and metabolisms pathways appear to act as hubs that connect other trait-associated pathways to influence the severity of social communication deficits inherent to ASD. Both RF and SVM algorithms can yield a classification accuracy level >90% when all 191 DERs were analyzed. The ASD subtypes defined by the presence of language impairment, a strong indicator for prognosis, can be predicted by transcriptomic profiles associated with social communication deficits and cholesterol biosynthesis and metabolism. Conclusion: The results suggest that both RF and SVM are acceptable options for machine learning algorithms to identify AD subgroups characterized by clinical homogeneity related to prognosis

Algorithm · Autism · Autism spectrum disorder · Biology · DNA microarray · Gene · Gene expression · Machine learning · Microarray · Microarray analysis techniques · Psychiatry · Random forest · Support vector machine · Autism Spectrum Disorder Research · Computer Science · Genetics and Neurodevelopmental Disorders · Medicine · Virology and Viral Diseases · Artificial Intelligence · Genetics

• Why not try to predict autism spectrum disorder with crucial biomarkers in cuproptosis signaling pathway

  Open Access•Yu Zhou, Jing Gao•Frontiers in Psychiatry•2022

  Incluir item

• Support-Vector Networks

  Open Access•Corinna Cortes, Vladimir Vapnik•Machine Learning•1995

  Incluir item

• Autism Diagnostic Interview-Revised

  Open Access•Catherine Lord, Michael Rutter et al.•Journal of Autism and Developmental…•1994

  Incluir item

• Kegg

  Open Access•Minoru Kanehisa•Nucleic Acids Research•2000

  Incluir item

• Building Predictive Models in R Using the caret Package

  Open Access•Max Kühn•Journal of Statistical Software•2008

  Incluir item

• Standardizing Ados Scores for a Measure of Severity in Autism Spectrum Disorders

  Open Access•Katherine Gotham, Anthony Pickles et al.•Journal of Autism and Developmental…•2009

  Incluir item

• Random Forests

  Open Access•Leo Breiman•Machine Learning•2001

  Incluir item