DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes
Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accr...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/4826206 |
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| _version_ | 1849411338538844160 |
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| author | Pi-Jing Wei Di Zhang Hai-Tao Li Junfeng Xia Chun-Hou Zheng |
| author_facet | Pi-Jing Wei Di Zhang Hai-Tao Li Junfeng Xia Chun-Hou Zheng |
| author_sort | Pi-Jing Wei |
| collection | DOAJ |
| description | Integration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes. |
| format | Article |
| id | doaj-art-60376390fe6c46bfa079bb20c5d10b23 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-60376390fe6c46bfa079bb20c5d10b232025-08-20T03:34:48ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/48262064826206DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver GenesPi-Jing Wei0Di Zhang1Hai-Tao Li2Junfeng Xia3Chun-Hou Zheng4College of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, ChinaCollege of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, ChinaState Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210018, ChinaInstitute of Health Sciences, Anhui University, Hefei, Anhui 230601, ChinaCollege of Computer Science and Technology, Anhui University, Hefei, Anhui 230601, ChinaIntegration of multi-omics data of cancer can help people to explore cancers comprehensively. However, with a large volume of different omics and functional data being generated, there is a major challenge to distinguish functional driver genes from a sea of inconsequential passenger genes that accrue stochastically but do not contribute to cancer development. In this paper, we present a gene length-based network method, named DriverFinder, to identify driver genes by integrating somatic mutations, copy number variations, gene-gene interaction network, tumor expression, and normal expression data. To illustrate the performance of DriverFinder, it is applied to four cancer types from The Cancer Genome Atlas including breast cancer, head and neck squamous cell carcinoma, thyroid carcinoma, and kidney renal clear cell carcinoma. Compared with some conventional methods, the results demonstrate that the proposed method is effective. Moreover, it can decrease the influence of gene length in identifying driver genes and identify some rare mutated driver genes.http://dx.doi.org/10.1155/2017/4826206 |
| spellingShingle | Pi-Jing Wei Di Zhang Hai-Tao Li Junfeng Xia Chun-Hou Zheng DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes Complexity |
| title | DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes |
| title_full | DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes |
| title_fullStr | DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes |
| title_full_unstemmed | DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes |
| title_short | DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes |
| title_sort | driverfinder a gene length based network method to identify cancer driver genes |
| url | http://dx.doi.org/10.1155/2017/4826206 |
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