Rogers MF, Gaunt TR, Campbell C. Prediction of driver variants in the cancer genome via machine learning methodologies.
Brief Bioinform 2021;
22:bbaa250. [PMID:
33094325 PMCID:
PMC8293831 DOI:
10.1093/bib/bbaa250]
[Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 09/04/2020] [Accepted: 09/06/2020] [Indexed: 01/18/2023] Open
Abstract
Sequencing technologies have led to the identification of many variants in the human genome which could act as disease-drivers. As a consequence, a variety of bioinformatics tools have been proposed for predicting which variants may drive disease, and which may be causatively neutral. After briefly reviewing generic tools, we focus on a subset of these methods specifically geared toward predicting which variants in the human cancer genome may act as enablers of unregulated cell proliferation. We consider the resultant view of the cancer genome indicated by these predictors and discuss ways in which these types of prediction tools may be progressed by further research.
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