Ontology-based prediction of cancer driver genes
| Autor: | Ashraf Dallol, Georgios V. Gkoutos, Rolina Al-Wassia, Shadi S. Alkhayyat, Sara Althubaiti, Robert Hoehndorf, Takashi Gojobori, Katsuhiko Mineta, Paul N. Schofield, Andrew D Beggs, Adeeb Noor, Andreas Karwath |
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| Přispěvatelé: | Althubaiti, Sara [0000-0001-5754-8569], Dallol, Ashraf [0000-0002-8803-228X], Mineta, Katsuhiko [0000-0002-4727-045X], Beggs, Andrew D [0000-0003-0784-2967], Schofield, Paul N [0000-0002-5111-7263], Hoehndorf, Robert [0000-0001-8149-5890], Apollo - University of Cambridge Repository, Beggs, Andrew D. [0000-0003-0784-2967], Schofield, Paul N. [0000-0002-5111-7263] |
| Rok vydání: | 2019 |
| Předmět: |
Colorectal cancer
lcsh:Medicine 02 engineering and technology medicine.disease_cause Machine Learning 0302 clinical medicine Neoplasms Exome lcsh:Science Cancer genetics 0303 health sciences Mutation Multidisciplinary article High-Throughput Nucleotide Sequencing Genomics Phenotype 3. Good health 030220 oncology & carcinogenesis Identification (biology) 139 141 0206 medical engineering 631/114/2403 Computational biology Biology 03 medical and health sciences Biomarkers Tumor medicine Humans Genetic Predisposition to Disease Gene Genetic Association Studies 030304 developmental biology Whole genome sequencing Genetic heterogeneity lcsh:R Computational Biology Cancer Molecular Sequence Annotation Oncogenes 631/114/1305 medicine.disease Gene Ontology 692/4028/67/68 ROC Curve lcsh:Q 119 human activities 020602 bioinformatics |
| Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-9 (2019) Scientific Reports |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-019-53454-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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