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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples
Gao, Bo1,2; Zhao, Yue3,4; Li, Yang1,2; Liu, Juntao1; Wang, Lushan2; Li, Guojun1,2; Su, Zhengchang5
2019-02-20
Source PublicationADVANCED SCIENCE
ISSN2198-3844
Volume6Issue:4Pages:9
AbstractMutual 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/.
Keywordcancer genomics coverage driver modules exclusivity signaling networks
DOI10.1002/advs.201801384
Language英语
Funding ProjectNational Natural Science Foundation of China[61432010] ; National Natural Science Foundation of China[61771009] ; National Key Research and Development Program of China[2016YFB0201702] ; US National Science Foundation[DBI-1661332] ; NIH[R01GM106013] ; program for outstanding PhD candidate of Shandong University
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Materials Science
WOS SubjectChemistry, Multidisciplinary ; Nanoscience & Nanotechnology ; Materials Science, Multidisciplinary
WOS IDWOS:000459168700003
PublisherWILEY
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/32598
Collection中国科学院数学与系统科学研究院
Affiliation1.Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China
2.Shandong Univ, State Key Lab Microbial Technol, Jinan 250100, Shandong, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, IAM, MADIS,NCMIS, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
5.Univ North Carolina Charlotte, Coll Comp & Informat, Dept Bioinformat & Genom, 9201 Univ City Blvd, Charlotte, NC 28223 USA
Recommended Citation
GB/T 7714
Gao, Bo,Zhao, Yue,Li, Yang,et al. Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples[J]. ADVANCED SCIENCE,2019,6(4):9.
APA Gao, Bo.,Zhao, Yue.,Li, Yang.,Liu, Juntao.,Wang, Lushan.,...&Su, Zhengchang.(2019).Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples.ADVANCED SCIENCE,6(4),9.
MLA Gao, Bo,et al."Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples".ADVANCED SCIENCE 6.4(2019):9.
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