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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples
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| Zeitschriftentitel: | Advanced Science |
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| Personen und Körperschaften: | , , , , , , |
| In: | Advanced Science, 6, 2019, 4 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Wiley
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| Schlagwörter: |
| author_facet |
Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang |
|---|---|
| author |
Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang |
| spellingShingle |
Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang Advanced Science Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples General Physics and Astronomy General Engineering Biochemistry, Genetics and Molecular Biology (miscellaneous) General Materials Science General Chemical Engineering Medicine (miscellaneous) |
| author_sort |
gao, bo |
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Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang 2198-3844 2198-3844 Wiley General Physics and Astronomy General Engineering Biochemistry, Genetics and Molecular Biology (miscellaneous) General Materials Science General Chemical Engineering Medicine (miscellaneous) http://dx.doi.org/10.1002/advs.201801384 <jats:title>Abstract</jats:title><jats:p>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: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://sourceforge.net/projects/cancer-pathway/files/">https://sourceforge.net/projects/cancer‐pathway/files/</jats:ext-link>.</jats:p> Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples Advanced Science |
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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_full |
Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
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Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_short |
Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_sort |
prediction of driver modules via balancing exclusive coverages of mutations in cancer samples |
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General Physics and Astronomy General Engineering Biochemistry, Genetics and Molecular Biology (miscellaneous) General Materials Science General Chemical Engineering Medicine (miscellaneous) |
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http://dx.doi.org/10.1002/advs.201801384 |
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2019 |
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<jats:title>Abstract</jats:title><jats:p>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: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://sourceforge.net/projects/cancer-pathway/files/">https://sourceforge.net/projects/cancer‐pathway/files/</jats:ext-link>.</jats:p> |
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| description | <jats:title>Abstract</jats:title><jats:p>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: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://sourceforge.net/projects/cancer-pathway/files/">https://sourceforge.net/projects/cancer‐pathway/files/</jats:ext-link>.</jats:p> |
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| spelling | Gao, Bo Zhao, Yue Li, Yang Liu, Juntao Wang, Lushan Li, Guojun Su, Zhengchang 2198-3844 2198-3844 Wiley General Physics and Astronomy General Engineering Biochemistry, Genetics and Molecular Biology (miscellaneous) General Materials Science General Chemical Engineering Medicine (miscellaneous) http://dx.doi.org/10.1002/advs.201801384 <jats:title>Abstract</jats:title><jats:p>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: <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://sourceforge.net/projects/cancer-pathway/files/">https://sourceforge.net/projects/cancer‐pathway/files/</jats:ext-link>.</jats:p> Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples Advanced Science |
| spellingShingle | Gao, Bo, Zhao, Yue, Li, Yang, Liu, Juntao, Wang, Lushan, Li, Guojun, Su, Zhengchang, Advanced Science, Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples, General Physics and Astronomy, General Engineering, Biochemistry, Genetics and Molecular Biology (miscellaneous), General Materials Science, General Chemical Engineering, Medicine (miscellaneous) |
| title | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_full | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_fullStr | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_full_unstemmed | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_short | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| title_sort | prediction of driver modules via balancing exclusive coverages of mutations in cancer samples |
| title_unstemmed | Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples |
| topic | General Physics and Astronomy, General Engineering, Biochemistry, Genetics and Molecular Biology (miscellaneous), General Materials Science, General Chemical Engineering, Medicine (miscellaneous) |
| url | http://dx.doi.org/10.1002/advs.201801384 |