一种基于突变基因网络的癌症驱动通路识别算法

doi:10.11896／j.issn.1002-137X.2018.07.040

Abstract

Abstract: Large cancer genome projects such as The Cancer Genome Atlas(TCGA) and International Cancer Genome Consortium(ICGC) have produced big amount of data collected from patients with different cancer types.The identification of mutated genes causing cancer is a significant challenge.Genovariation in cancer cells can be divided into two types:functional driver mutation and random passenger mutation.Identifcation of driver genes is benefit to understand the pathogenesis and progression of cancer,as well as research cancer drug and targeted therapy,and it is an essential problem in the field of bioinformatics.This paper proposed a driver pathway identification algorithm based on mutated gene networks for cancer(GNDP).In GNDP,a nonoverlap balance metric is defined to measure the possibility of two genes lying in the same driver pathway.To reduce the complexity of the constructed mutually exclusive gene networks,the nonoverlap balance metric,the exclusivity and the coverage of a gene pair are computed first,and then the edges with low nonoverlap balance metric,low exclusivity and low coverage are deleted.Then,all maximal cliques which might be potential driver pathways are found out.After that,the weight of each clique is assigned as the product of its exclusive degree and coverage degree and then every node of a clique will be checked to judge whether is’s deletion might obtain a larger weight.At last,the maximal weight cliques are obtained in mutually exclusive gene networks as the final driver pathways.This paper compared GNDP algorithm with classical algorithm Dendrix and Multi-Dendrix on both simulated data sets and somatic mutation data sets.The results show that GNDP can detect all artificial pathways in simulated data.For Lung adenocarcinoma and Glioblastoma data,GNDP shows higher efficiency and accuracy than the comparison algorithms.In addition,GNDP does not need any prior knowledge and does not need to set the number of genes in driver pathways in advance.

Key words: Cancer genome, Driver pathways, Gene networks, Maximal cliques, Somatic mutation

CLC Number:

TP311

GUO Bing, ZHENG Wen-ping, HAN Su-qing. Driver Pathway Identification Algorithm Based on Mutated Gene Networks for Cancer[J].Computer Science, 2018, 45(7): 230-236.

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Driver Pathway Identification Algorithm Based on Mutated Gene Networks for Cancer

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