SVM-Prot: Web-Based Support Vector Machine Software for Functional Classification of a Protein from Its Primary Sequence. Cai, C., Han, L., Ji, Z., Chen, X., & Chen, Y. Nucleic Acids Res., 31(13):3692--3697, OXFORD UNIV PRESS, GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND, JUL 1, 2003. doi abstract bibtex Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.
@article{Cai:2003kl,
Abstract = {Prediction of protein function is of significance in studying biological processes. One approach for function prediction is to classify a protein into functional family. Support vector machine (SVM) is a useful method for such classification, which may involve proteins with diverse sequence distribution. We have developed a web-based software, SVMProt, for SVM classification of a protein into functional family from its primary sequence. SVMProt classification system is trained from representative proteins of a number of functional families and seed proteins of Pfam curated protein families. It currently covers 54 functional families and additional families will be added in the near future. The computed accuracy for protein family classification is found to be in the range of 69.1-99.6\%. SVMProt shows a certain degree of capability for the classification of distantly related proteins and homologous proteins of different function and thus may be used as a protein function prediction tool that complements sequence alignment methods. SVMProt can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.},
Address = {GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND},
Author = {Cai, C.Z. and Han, L.Y. and Ji, Z.L. and Chen, X. and Chen, Y.Z.},
Date = {JUL 1 2003},
Date-Added = {2009-03-06 16:31:42 -0500},
Date-Modified = {2009-03-06 16:34:54 -0500},
Doi = {DOI 10.1093/nar/gkg600},
Isi = {000183832900088},
Isi-Recid = {130287265},
Isi-Ref-Recids = {116331889 116024937 122960810 107285358 115616056 119951411 128392803 107589924 104608165 107093156 130287266 123654644 119601650 92824492 115309134 104563095 112515735 124154114 116024945 81902218 102480509 118289492 119804118 116331927 123468752 118289462 111148860 113165582 122967912 118718100 122070370 115305775 122960686 107285354 120325701 113323812 128392635 127632843 125815561},
Journal = {Nucleic Acids Res.},
Keywords = {protein; function; svm; sequence;},
Month = {JUL 1},
Number = {13},
Pages = {3692--3697},
Publisher = {OXFORD UNIV PRESS},
Timescited = {77},
Title = {{SVM-Prot}: Web-Based Support Vector Machine Software for Functional Classification of a Protein from Its Primary Sequence},
Volume = {31},
Year = {2003},
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