Prediction of driver variants in the cancer genome via machine learning methodologies

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

*Corresponding author for this work

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

9 Citations (Scopus)
100 Downloads (Pure)


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
Article numberbbaa250
Number of pages10
JournalBriefings in Bioinformatics
Publication statusPublished - 22 Oct 2020

Bibliographical note

Provisional acceptance date added, based on record creation


  • varient prediction
  • cancer
  • machine learning


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