Sorting the phenotypic heterogeneity of autism spectrum disorders: A hierarchical clustering model
Autism spectrum disorder (ASD) is characterized by notable phenotypic heterogeneity, which is often viewed as an obstacle to the study of its etiology, diagnosis, treatment, and prognosis. Heterogeneity in ASD is multidimensional and complex including variability in phenotype as well as clinical, physiologic, and pathologic parameters. We apply a hierarchical clustering model suited to dealing with datasets of mixed data types to stratify children with ASD into more homogeneous subgroups in line with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 model. The results of this cluster analysis will provide a better understanding the complex issue of ASD phenotypic heterogeneity and identify subgroups useful for further ASD genetic studies. Our goal is to provide insight into viable phenotypic and genotypic markers that would guide further cluster analysis of ASD genetic data. We suggest that analyzing the clusters in a hierarchical structure is a well-suited and meaningful model to unravel the complex heterogeneity of this disorder.
autism spectrum disorder, data mining, hierarchical clustering, machine learning
Obafemi-Ajayi, Tayo, Dao Lam, T. Nicole Takahashi, Stephen Kanne, and Donald Wunsch. "Sorting the phenotypic heterogeneity of autism spectrum disorders: A hierarchical clustering model." In 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1-7. IEEE, 2015.
2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2015