Prediction of Driver Variants in the Cancer Genome via Machine Learning Methodologies

Mark F Rogers, Tom R Gaunt, I C G Campbell

Research output: Contribution to journalReview article (Academic Journal)peer-review

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 towards 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.
Original languageEnglish
JournalBriefings in Bioinformatics
Publication statusAccepted/In press - 6 Sep 2020

Bibliographical note

Provisional acceptance date added, based on record creation

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