Medical School Office Building (MSOB)
|DATE:||November 30, 2017|
|TIME:||1:30 - 2:50 pm|
|TITLE:||Statistical learning approaches to understand the genetic and phenotypic complexity of autism|
||Dennis P. Wall
Associate Professor of Pediatrics (Systems Medicine), of Biomedical Data Science and, by courtesy,
of Psychiatry and Behavioral Sciences, Stanford
I will discuss my lab’s efforts to use machine learning to build practical models for better autism care and to tie genotype to phenotype. I will begin by defining autism and why & how it represents a quintessential translational bioinformatics challenge. Then, I will describe our use of machine learning , including sparsifying models to find top ranked features that align well clinical diagnosis but that can be used in mobile detection efforts. Next, I will discuss our use of generalized low rank models  to impute missingness and improve our ability to define the forms of autism. Finally, I will describe approaches to recode the genome of autism subjects into vectors that can be used for prioritizing genes via coalitional game theory  and that can also be used for predicting phenotype(s).
 Kosmicki, J., Sochat, V., & Wall, D.P. (2015). Searching for a minimal set of behaviors for autism detection through feature selection based machine learning. Nature Translational Psychiatry, v.5(2): e514. doi:10.1038/tp.2015.7.
 Udell M, Horn C, Zadeh R, Boyd S. Generalized low rank models. Foundations and Trends in Machine Learning. 2016 Jun 23; 9(1):1-118.
 Esteban, F. & Wall D.P. (2009). Using game theory to detect genes involved in Autism Spectrum Disorder. TOP. July 2011, Vol. 19, Issue 1, pp 121-129. (ISSN 1863-8279).