Ruhollah Shemirani

TL;DR: A framework for repurposing data from EHRs in concert with genomic data to explore the demographic ties that can impact disease burdens and demonstrates that fine-scale population structure can impact the prediction of complex disease risk within groups.

TL;DR: It is shown that, on 12 cancer RNA-Seq data sets ranging from 323 to 1,210 samples, using either a random forests-based gene selection method or the EPS pipeline outperforms differential expression analysis for 9 and 8 out of the 12 datasets respectively, in terms of identifying subsets of genes associated with survival.

TL;DR: iLASH enables fast and accurate detection of IBD, an upstream step in applications of I BD for population genetics and trait mapping, making IBD estimation tractable for hundreds of thousands to millions of individuals.

TL;DR: In this article, a framework for repurposing data from Electronic Health Records (EHRs) in concert with genomic data to explore enrichment of disease within sub-populations was proposed.

TL;DR: In this paper, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals.