Intelligent Systems Laboratory, University of Bristol, Bristol, BS8 1UB, United Kingdom.
MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, BS8 2BN, United Kingdom.
Sci Rep. 2017 Sep 14;7(1):11597. doi: 10.1038/s41598-017-11746-4.
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/ .
对于基因组编码区和非编码区的体细胞点突变，我们提出了 CScape，这是一种用于预测突变成为癌症驱动因素可能性的综合分类器。在体细胞突变的测试中，CScape 往往优于其他方法，在编码区达到 91%的平衡准确率，在非编码区达到 70%，而使用阈值来隔离高可信度的预测，则可以实现更高的准确率。阳性预测往往聚集在基因组区域中，因此我们应用一种统计方法来分离癌症基因组中编码区和非编码区，这些区域似乎富含高可信度的预测疾病驱动因素。预测结果和软件可在 http://CScape.biocompute.org.uk/ 上获取。
