Alkhayyat, Shadi Salem
Beggs, Andrew D.
Schofield, Paul N.
Gkoutos, Georgios V.
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
Biological and Environmental Sciences and Engineering (BESE) Division
Online Publication Date2019-11-22
Print Publication Date2019-12
Permanent link to this recordhttp://hdl.handle.net/10754/660436
MetadataShow full item record
AbstractIdentifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity many driver mutations within a gene occur at low frequencies, which make it challenging to distinguish them from non-driver mutations. We have developed a novel method for identifying cancer driver genes. Our approach utilizes multiple complementary types of information, specifically cellular phenotypes, cellular locations, functions, and whole body physiological phenotypes as features. We demonstrate that our method can accurately identify known cancer driver genes and distinguish between their role in different types of cancer. In addition to confirming known driver genes, we identify several novel candidate driver genes. We demonstrate the utility of our method by validating its predictions in nasopharyngeal cancer and colorectal cancer using whole exome and whole genome sequencing.
CitationAlthubaiti, S., Karwath, A., Dallol, A., Noor, A., Alkhayyat, S. S., Alwassia, R., … Hoehndorf, R. (2019). Ontology-based prediction of cancer driver genes. Scientific Reports, 9(1). doi:10.1038/s41598-019-53454-1
PubMed Central IDPMC6874647
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific Reports
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