KatzDriver：一种基于GR网络的癌症致病基因预测方法
KatzDriver: A network Based method to predict cancer causal genes in GR Network
摘要：
肿瘤学中的一个重要问题是找到干扰细胞功能并导致癌症传播的基因。基因，即驱动基因，当它们在表达中突变时，通过激活突变的蛋白质导致癌症。因此，许多方法被引入来预测这组基因。这些大多是基于每个基因突变数量的计算方法。最近，一些基于网络的方法已被提出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相对重要性的每个基因在传播和吸收的基因异常的网络识别CDG。实验结果表明，我们提出的算法是优于其他19个以前的方法，在准确性，精度和识别的CDG的数量。
英文摘要：
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.
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