Type
ArticleAuthors
Althubaiti, Sara
Karwath, Andreas
Dallol, Ashraf

Noor, Adeeb
Alkhayyat, Shadi Salem
Alwassia, Rolina
Mineta, Katsuhiko

Gojobori, Takashi

Beggs, Andrew D.

Schofield, Paul N.

Gkoutos, Georgios V.
Hoehndorf, Robert

KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Bioscience Research Center (CBRC)
Bioscience Program
Biological and Environmental Sciences and Engineering (BESE) Division
Date
2019-11-22Online Publication Date
2019-11-22Print Publication Date
2019-12Permanent link to this record
http://hdl.handle.net/10754/660436
Metadata
Show full item recordAbstract
Identifying 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.Citation
Althubaiti, 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/s41598-019-53454-1Publisher
Springer NatureJournal
Scientific ReportsPubMed ID
31757986PubMed Central ID
PMC6874647Additional Links
http://www.nature.com/articles/s41598-019-53454-1ae974a485f413a2113503eed53cd6c53
10.1038/s41598-019-53454-1
Scopus Count
Except where otherwise noted, this item's license is described as Archived with thanks to Scientific Reports
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