Applications of node-based resilience graph theoretic framework to clustering autism spectrum disorders phenotypes

Applied Network Science
John MattaTayo Obafemi-Ajayi

Abstract

With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better understanding of ASD phenotypic heterogeneity useful for further ASD studies. We address appropriate graph constructions suited for representing the ASD phenotype data. The sample population is drawn from a very large rigorous dataset: Simons Simplex Collection (SSC). Analysis of the results performed using graph quality measures, internal cluster validation measures, and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets. We also conduct feature extraction analysis to characterize relevant ...Continue Reading

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Citations

Nov 13, 2019·Current Pharmaceutical Design·Shogo NakashimaTatsuya Akutsu
May 8, 2021·Clinical Psychology Review·Joost A Agelink van RentergemHilde M Geurts

Methods Mentioned

BETA
feature extraction
Feature Selection

Related Concepts

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Autism

Autism spectrum disorder is associated with challenges with social skills, repetitive behaviors, and often accompanied by sensory sensitivities and medical issues. Here is the latest research on autism.

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