Speaker: Chris Amos, Ph.D.
Director of the Institute for Clinical and Translational Research, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine

Title: Understanding Complex Etiology of Diseases by Application of Machine Learning Tools

Abstract: Genetic analyses have identified etiological factors for several complex disease but understanding how genetic factors interact to increase risk is challenging.  In this talk I describe the development of novel methods for large-scale identification of population-level attributes that may affect genome-wide association studies.  Because studies we conduct are planned for hundreds of thousands of samples, we had to develop machine-learning based procedures that could be generalized for identifying ethnic variability.  We also evaluated tools for jointly estimating the effects of multiple genetic factors by using machine learning tools including random forests and classification trees.  These particular analytical schemes identified novel interactions among alleles at multiple loci influencing risk for a rare autoimmune disease. We are now studying the efficacy of modeling with classification tree based analysis versus more traditional approaches such as logistic regression with lasso for high dimensional SNP studies.

Bio3 Seminar Series sponsored by 
Department of Biostatistics, Bioinformatics & Biomathematics (DBBB)

Friday, February 14 at 10:00am to 11:00am

Building D, Warwick Evans Conference Room, First Flr 4000 Reservoir Road, N.W., Washington

Event Type

Academic Events


Students, Faculty/Staff


Georgetown University Medical Center, Lombardi Comprehensive Cancer Center, Biomedical Graduate Education, Biostatistics Graduate Program



Chris Amos, Ph.D.

Event Contact Name

Veronica Chiu

Event Contact Email

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