Title: Towards Reliable Experimental Recommendations of Gene-gene Interactions for Cardiac Hypertrophy

Presented by Tiffany Tang
Department of Statistics, University of Michigan

Abstract: Given the continual influx of data in the biomedical field, the question arises: Can we extract interpretable, actionable insights from the data and generate reliable scientific hypotheses to inform future wet-lab experiments? In this talk, we dive into a particular case study, where we built an end-to-end pipeline to hypothesize and experimentally validate important gene-gene interaction drivers of cardiac hypertrophy, a common heart disease that presents as the enlargement and thickening of the heart wall and carries significant risk for heart failure and sudden cardiac death. In this highly interdisciplinary collaboration, we first developed the low-signal iterative random forest (lo-siRF), a data-driven computational prioritization method to recommend interaction candidates for follow-up knockdown experiments. Lo-siRF builds upon the computationally-efficient interaction search engine of iterative random forests but has been specifically tailored for low signal-to-noise data and incorporates a new stability-driven importance score for random forests. Subsequently, we performed gene silencing experiments to assess the gene-gene interactions recommended by lo-siRF. These silencing experiments confirmed the effects of our recommended interactions between two well-known drivers of cardiac hypertrophy (TTN and IGF1R) and CCDC141, a relatively unknown gene, expanding our understanding and scope of genetic regulation in cardiac structure.

A seminar tea will be held at 11:00 a.m. in University Office Plaza, Room 116. All are Welcome.