Ramsahai Emilie, Walkins Kheston, Tripathi Vrijesh, John Melford
Department of Mathematics & Statistics, The Faculty of Science and Technology, The University of the West Indies, St. Augustine Campus , Trinidad and Tobago.
Department of Preclinical Sciences, The University of the West Indies, St. Augustine , Trinidad and Tobago.
PeerJ. 2017 Jan 26;5:e2568. doi: 10.7717/peerj.2568. eCollection 2017.
Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.
生物信息学家已经实施了不同的策略来区分癌症驱动基因和乘客基因。最近的进展之一是采用面向通路的方法。采用这种策略的方法高度依赖于所使用的通路相互作用网络的质量和规模，并且需要强大的统计环境进行分析。R语言中有许多基因组库。DriverNet和DawnRank采用基于通路的方法，这些方法使用矩阵形式的基因相互作用图。我们研究了将来自3个不同来源的数据相结合对DriverNet和DawnRank预测癌症驱动基因结果的益处。由此得到了一个丰富的数据集，包含13862个基因和372250个相互作用，与它们的原始网络相比，其准确率分别提高了17%和28%。该研究确定了33个新的候选驱动基因。我们的研究突出了结合网络和加权边在识别癌症驱动基因方面提供更高准确性的潜力。
