Year: 2014
Type: dissertation
Abstract: A key challenge in cancer genomics is the identification and prioritization of genomic aberrations that potentially act as drivers of cancer.HIT'nDRIVE is a combinatorial method to identify aberrant genes that can collectively influence possibly distant "outlier" genes based on the "random-walk facility location" (RWFL) problem on an interaction network.RWFL uses "multi-hitting time", the expected minimum length of a random walk originating from any aberrant gene towards an outlier.HIT'nDRIVE aims to find the smallest set of aberrant genes from which one can reach outliers within desired multi-hitting time.It estimates multi-hitting time based on the independent hitting times and reduces the RWFL to a weighted multi-set cover problem, which it solves as an integer linear program (ILP).We apply HIT'nDRIVE to identify aberrant genes that potentially act as drivers in a cancer data set and make phenotype predictions using only the potential drivers, more accurately than alternative approaches.
Source: Summit (Simon Fraser University)
Author: Ermin Hodzic
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W57421243