title: KemaDom: a web server for domain prediction using kernel machine with local context.
abstract: 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.
authors.affiliation: Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, PR China.
authors.forename: Lusheng
authors.initials: L
authors.lastname: Chen
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Shaoping
authors.initials: S
authors.lastname: Ling
authors.forename: Caiyan
authors.initials: C
authors.lastname: Jia
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
mesh_terms: D001185:Artificial Intelligence; D020407:Internet; D017434:Protein Structure, Tertiary; D012984:Software