SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence
SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence
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DOI：
10.1093/nar/gkg600
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发表时间：
2003-07-01
影响因子：
14.9
通讯作者：
Chen, YZ
中科院分区：
文献类型：
--
作者：
Cai, CZ;Han, LY;Chen, YZ
蛋白质功能预测在生物过程研究中具有重要意义。功能预测的一种方法是将蛋白质分类为功能家族。支持向量机是一种有用的分类方法，它可能涉及不同序列分布的蛋白质。我们开发了一个基于网络的软件SVMProt，用于支持向量机将蛋白质从其一级序列分类为功能家族。SVMProt分类系统是从一些功能家族的代表蛋白和Pfam蛋白家族的种子蛋白中训练而来的。它目前涵盖54个功能家庭，不久的将来将增加更多的家庭。蛋白质家族分类的计算准确率在69.1-99.6%之间。SVMProt对功能不同的远缘蛋白质和同源蛋白质有一定的分类能力，可作为蛋白质功能预测工具，补充序列比对方法。SVMProt可以在http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi.上访问。
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.