Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes
Heterogeneity in Autism Spectrum Disorder (ASD) is complex including variability in behavioral phenotype as well as clinical, physiologic, and pathologic parameters. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) now diagnoses ASD using a 2-dimensional model based social communication deficits and fixated interests and repetitive behaviors. Sorting out heterogeneity is crucial for study of etiology, diagnosis, treatment and prognosis. In this paper, we present an ensemble model for analyzing ASD phenotypes using several machine learning techniques and a k-dimensional subspace clustering algorithm. Our ensemble also incorporates statistical methods at several stages of analysis. We apply this model to a sample of 208 probands drawn from the Simon Simplex Collection Missouri Site patients. The results provide useful evidence that is helpful in elucidating the phenotype complexity within ASD. Our model can be extended to other disorders that exhibit a diverse range of heterogeneity.
Al-Jabery, Khalid, Tayo Obafemi-Ajayi, Gayla R. Olbricht, T. Nicole Takahashi, Stephen Kanne, and Donald Wunsch. "Ensemble statistical and subspace clustering model for analysis of autism spectrum disorder phenotypes." In 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3329-3333. IEEE, 2016.
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS