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

Journal: Briefings in bioinformatics
Published Date:

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.

Authors

  • Mark F Rogers
    Intelligent Systems Laboratory, University of Bristol, Bristol, UK.
  • Tom R Gaunt
    MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol BS82BN, UK.
  • Colin Campbell
    Intelligent Systems Laboratory, University of Bristol, Bristol BS81UB, UK.