The combination approach of svm and ecoc for powerful identification and classification of transcription factor
文献类型：期刊论文
| 作者 | Zheng, Guangyong1,2,3; Qian, Ziliang3,6; Yang, Qing1; Wei, Chaochun4,5; Xie, Lu5; Zhu, Yangyong1,5; Li, Yixue4,5 |
| 刊名 | Bmc bioinformatics
 |
| 出版日期 | 2008-06-16 |
| 卷号 | 9页码:8 |
| ISSN号 | 1471-2105 |
| DOI | 10.1186/1471-2105-9-282 |
| 通讯作者 | Zhu, yangyong(yyzhu@fudan.edu.cn) |
| 英文摘要 | Background: transcription factors ( tfs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. traditionally, they were identified and classified through experimental approaches. in order to save time and reduce costs, many computational methods have been developed to identify tfs from new proteins and to classify the resulted tfs. though these methods have facilitated screening of tfs to some extent, low accuracy is still a common problem. with the fast growing number of new proteins, more precise algorithms for identifying tfs from new proteins and classifying the consequent tfs are in a high demand. results: the support vector machine ( svm) algorithm was utilized to construct an automatic detector for tf identification, where protein domains and functional sites were employed as feature vectors. error- correcting output coding ( ecoc) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine ( svm) methodology for tf classification. the overall success rates of identification and classification achieved 88.22% and 97.83% respectively. finally, a web site was constructed to let users access our tools ( see availability and requirements section for url). conclusion: the svm method was a valid and stable means for tfs identification with protein domains and functional sites as feature vectors. error- correcting output coding ( ecoc) algorithm is a powerful method for multi- class classification problem. when combined with svm method, it can remarkably increase the accuracy of tf classification using protein domains and functional sites as feature vectors. in addition, our work implied that ecoc algorithm may succeed in a broad range of applications in biological data mining. |
| WOS关键词 | FUNCTIONAL DOMAIN COMPOSITION ; SUPPORT VECTOR MACHINES ; DNA-BINDING PROTEINS ; PREDICTION ; DATABASE ; RECOGNITION ; TRANSFAC(R) ; JACKKNIFE ; PROFILES ; SERVER |
| WOS研究方向 | Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| WOS类目 | Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:000257161400001 |
| 出版者 | BIOMED CENTRAL LTD |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2390981 |
| 专题 | 中国科学院大学 |
| 通讯作者 | Zhu, Yangyong |
| 作者单位 | 1.Fudan Univ, Dept Comp & Informat Technol, Shanghai 200433, Peoples R China 2.Fudan Univ, Sch Life Sci, Shanghai 200433, Peoples R China 3.Chinese Acad Sci, Shanghai Inst Biol Sci, Key Lab Syst Biol, Bioinformat Ctr, Shanghai 200031, Peoples R China 4.Shanghai Jiao Tong Univ, Coll Life Sci & Technol, Shanghai 200240, Peoples R China 5.Shanghai Ctr Bioinformat Technol, Shanghai 200235, Peoples R China 6.Chinese Acad Sci, Grad Sch, Beijing 100039, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Guangyong,Qian, Ziliang,Yang, Qing,et al. The combination approach of svm and ecoc for powerful identification and classification of transcription factor[J]. Bmc bioinformatics,2008,9:8. |
| APA | Zheng, Guangyong.,Qian, Ziliang.,Yang, Qing.,Wei, Chaochun.,Xie, Lu.,...&Li, Yixue.(2008).The combination approach of svm and ecoc for powerful identification and classification of transcription factor.Bmc bioinformatics,9,8. |
| MLA | Zheng, Guangyong,et al."The combination approach of svm and ecoc for powerful identification and classification of transcription factor".Bmc bioinformatics 9(2008):8. |
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来源：中国科学院大学
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