Projects per year
For somatic point mutations in coding and non-coding regions of the genome, we propose CScape, an integrative classifier for predicting the likelihood that mutations are cancer drivers. Tested on somatic mutations, CScape tends to outperform alternative methods, reaching 91% balanced accuracy in coding regions and 70% in non-coding regions, while even higher accuracy may be achieved using thresholds to isolate high-confidence predictions. Positive predictions tend to cluster in genomic regions, so we apply a statistical approach to isolate coding and non-coding regions of the cancer genome that appear enriched for high-confidence predicted disease-drivers. Predictions and software are available at http://cscape.biocompute.org.uk/.
|Number of pages||11|
|Publication status||Published - 14 Sep 2017|
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- 4 Finished
Novel Methodology for Predicting the Functional Effects of Genetic Variation
1/06/15 → 31/05/18
An Active Learning Approach to Network Inference
1/07/13 → 30/06/16
IEU Theme 2
Flach, P. A., Gaunt, T. R. & Gaunt, T. R.
1/06/13 → 31/03/18