Seminar: Genome-wide prioritization of autism spectrum disorder risk genes using network science and

Yu Zhang
M.Sc. Thesis Proposal
Supervisor: Dr. Ting Hu

Genome-wide prioritization of autism spectrum disorder risk genes using network science and evolutionary computing

Department of Computer Science
Friday, November 23, 2018, 1:50p.m., Room EN 2022


Abstract

Autism spectrum disorder (ASD) is a neuropsychiatric disorder characterized by impairments in
reciprocal social interaction and communication, and the presence of restricted and repetitive behaviors. ASD is predominantly heritable, but the underlying genetic determinants are still largely unknown. Although over one thousand genes have been estimated to shown association with ASD, only a small fraction are known with strong genetic evidence from sequencing studies. In addition, the affected neural circuits and cell types of ASD individuals remain unclear and may vary at different developmental stages. Over the past decades, rapid advances in network biology indicate that molecular networks offer a  powerful conceptual framework that could potentially revolutionize our view of disease pathologies. Moreover, genetic programming (GP), an evolutionary algorithm, is an advanced and intelligent analytical tool, which can be used to unravel the disease-gene association with great potential. In this thesis, we propose to predict ASD-associated genes on a genome-wide scale using network science and evolutionary computing. Following the hypothesis that genes similar to known ASD-associated genes may potentially contribute to ASD susceptibility as well, we use biological networks of physical and functional gene interactions to characterize structural properties of genes as nodes in the networks. Then, by exploiting the structural properties, we use GP to learn predictive models that can prioritize novel candidate genes to what extent they are involved in ASD manifestation and help facilitate the downstream biological research to refine our understanding of the etiology of ASD, in order to improve clinical diagnosis, treat and even intervention of the disease.

Contact

Department of Computer Science

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