Applications of novel graph theoretic methods to clustering autism spectrum disorders phenotypes
This paper applies two node-based graph theoretic algorithms to cluster a set of 2680 Autism Spectrum Disorder (ASD) subjects using ASD phenotype features. The two node-based resilience measures are vertex attack tolerance (VAT) and integrity. Analysis of the results performed using internal cluster validation measures and clinical analysis outcome demonstrate the potential usefulness of resilience measure clustering for biomedical datasets.
Autism spectrum disorders, Clustering, Graph theory, Resilience measures
Matta, John, Thy Nguyen, Gunes Ercal, and Tayo Obafemi-Ajayi. "Applications of novel graph theoretic methods to clustering autism spectrum disorders phenotypes." In Proceedings of the International Conference on Bioinformatics and Computational Biology (BICOB), Honolulu, HI, USA, pp. 20-22. 2017.
Proceedings of the 9th International Conference on Bioinformatics and Computational Biology, BICOB 2017