Speaker: Hilal Kazan
Title: A random walk based method for cancer module discovery
Date/Time: April 24, 2019 / 13.40-14.30
Place: FENS G035
Abstract: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. I will describe a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusivity, and coverage to identify cancer driver modules. Our method outperforms several state-of-the-art computational methods on TCGA pancancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types.
BIO: Hilal Kazan is currently a faculty member at the Department of Computer Engineering in Antalya Bilim University. She obtained her PhD in Computer Science from University of Toronto in 2012. During her graduate studies, she also spent some time as a visiting researcher at Microsoft Research, Cambridge, UK. She holds a BSc in Computer Science and Engineering from Sabanci University. Dr.Kazan is interested in applying techniques from machine learning and graph theory to diverse sets of problems in computational biology. Her work has been published in prestigious journals such as Nature and Nature Biotechnology, and she has received grants from Tübitak and European Union.
Contact: Öznur Taştan