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Nucleic Acids Research 2006 34(Web Server issue):W158-W163; doi:10.1093/nar/gkl331
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© The Author 2006. Published by Oxford University Press. All rights reserved
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

Article

KemaDom: a web server for domain prediction using kernel machine with local context

Lusheng Chen1,2, Wei Wang3, Shaoping Ling4, Caiyan Jia1,2 and Fei Wang1,2,*

1 Shanghai Key Laboratory of Intelligent Information Processing, Fudan University Shanghai, PR China 2 Department of Computer Science and Engineering, School of Life Science, Fudan University Shanghai, PR China 3 Institute of Genetics, School of Life Science, Fudan University Shanghai, PR China 4 College of Information Engineering, Xiangtan University Xiangtan, Hunan, PR China

*To whom correspondence should be addressed. Tel: +86 21 5566 4712; Fax: +86 21 6565 4253; Email: wangfei{at}fudan.edu.cn.

Received February 13, 2006. Revised March 1, 2006. Accepted April 14, 2006.

Predicting domains of proteins is an important and challenging problem in computational biology because of its significant role in understanding the complexity of proteomes. Although many template-based prediction servers have been developed, ab initio methods should be designed and further improved to be the complementarity of the template-based methods. In this paper, we present a novel domain prediction system KemaDom by ensembling three kernel machines with the local context information among neighboring amino acids. KemaDom, an alternative ab initio predictor, can achieve high performance in predicting the number of domains in proteins. It is freely accessible at http://www.iipl.fudan.edu.cn/lschen/kemadom.htm and http://www.iipl.fudan.edu.cn/~lschen/kemadom.htm.

These authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authours


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