Sorting the Phenotypic Heterogeneity of Autism Spectrum Disorders: A Hierarchical Clustering Model

Abstract

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.

Meeting Name

2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2015: Aug. 12-15, Niagara Falls, Ontario, Canada)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

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

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2015 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Aug 2015

Link to Full Text

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