论文标题
KatzDriver：一种基于网络的方法，用于预测GR网络中的癌症因果基因
KatzDriver: A network Based method to predict cancer causal genes in GR Network
论文作者
论文摘要
肿瘤学的重要问题之一是找到扰动细胞功能并导致癌症传播的基因。这些基因在表达突变时，即驱动基因通过激活突变的蛋白质导致癌症。因此，已经引入了许多方法来预测这一基因。这些主要是基于每个基因突变的数量的计算方法。最近，已经提出了一些基于网络的方法来预测癌症驱动基因（CDG）。在这项研究中，我们使用基于网络的方法和每个基因的相对重要性在网络中基因异常的传播和吸收中识别CDG。将实验结果与以前的19种方法进行了比较，这些方法表明我们提出的算法在准确性，精度和公认CDG的数量方面比其他方法更好。
One of the important issues in oncology is finding the genes that perturbation the cell functionality, and result in cancer propagation. The genes, namely driver genes, when they mutate in expression, result in cancer through activation of the mutated proteins. So, many methods have been introduced to predict this group of genes. These are mostly computational methods based on the number of mutations of each gene. Recently, some network-based methods have been proposed to predict Cancer Driver Genes (CDGs). In this study, we use a network-based approach and relative importance of each gene in the propagation and absorption of genes anomalies in the network to recognize CDGs. The experimental results are compared with 19 previous methods that show our proposed algorithm is better than the others in terms of accuracy, precision, and the number of recognized CDGs.
