标题:Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples
作者:Gao, Bo; Zhao, Yue; Li, Yang; Liu, Juntao; Wang, Lushan; Li, Guojun; Su, Zhengchang
作者机构:[Gao, Bo; Li, Yang; Liu, Juntao; Li, Guojun] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.; [Gao, Bo; Li, Yang; Wang, Lushan; Li 更多
通讯作者:Li, Guojun;Li, GJ;Li, GJ;Su, ZC
通讯作者地址:[Li, GJ]Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China;[Li, GJ]Shandong Univ, State Key Lab Microbial Technol, Jinan 250100, Shandon 更多
来源:ADVANCED SCIENCE
出版年:2019
卷:6
期:4
DOI:10.1002/advs.201801384
关键词:cancer genomics; coverage; driver modules; exclusivity; signaling; networks
摘要:Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample-specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software is available at: https://sourcefoge.net/projects/cancer-pathway/files/.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058698975&doi=10.1002%2fadvs.201801384&partnerID=40&md5=c5352d34d7f3e882746ec86572c7c62e
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