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.
T. Obafemi-Ajayi et al., "Sorting the Phenotypic Heterogeneity of Autism Spectrum Disorders: A Hierarchical Clustering Model," Proceedings of the 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2015, Niagara Falls, Ontario, Canada), Institute of Electrical and Electronics Engineers (IEEE), Aug 2015.
The definitive version is available at http://dx.doi.org/10.1109/CIBCB.2015.7300337
2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2015: Aug. 12-15, Niagara Falls, Ontario, Canada)
Electrical and Computer Engineering
Center for High Performance Computing Research
Keywords and Phrases
Artificial Intelligence; Bioinformatics; Cluster Analysis; Data Mining; Diseases; Learning Systems; Autism Spectrum Disorders; Genetic Data; Genetic Studies; Hier-Archical Clustering; Hierarchical Structures; Mental Disorders; Mixed Data Types; Spectrum Analysis
Article - Conference proceedings
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