Machine Learning Approaches to Investigate the Relationship between Genetic Factors and Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a heterogeneous complex neurodevelopment disorder that affects 1 to 2 percent of global population. Over the past years, a sizeable body of research has expanded the knowledge about the pathogenesis of ASD, and identified a list of genotypes that are strongly related to the ASD. However, the complexity of genotype system forces researchers to explore novel approaches. Among all the approaches to understand the roles and interactions of these gene variants, using machine learning (ML) and big data analysis to discover the potential ASD related genotypes is a promising one, while only a few researchers have explored this field. We conducted a data analysis research based on the genetic data of ASD patients, provided a statistical approach to explore the genetics of ASD through genotypes, and identified an important weighted list of ASD related genotypes for further biological research.
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Liu, Zehai; Tian, Gengchen; Zhu, Jiarui; Chen, Weikang; Dou, Wanying; Lee, Chun Te; Chan, Chee Kai; and Dib, Omar, "Machine Learning Approaches to Investigate the Relationship between Genetic Factors and Autism Spectrum Disorder" (2021). Kean Publications. 896.