CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily
CTKPred: an SVM-based method for the prediction and classification of the cytokine superfamily
复制标题
DOI：
10.1093/protein/gzi041
复制
发表时间：
2005-08-01
影响因子：
2.4
通讯作者：
Sun, ZR
中科院分区：
文献类型：
--
作者：
Huang, N;Chen, H;Sun, ZR
细胞的增殖、分化和死亡是由大量的细胞-细胞信号控制的，失去这种控制会造成毁灭性的后果。在这些调节信号中，突出的是细胞因子超家族，它在免疫细胞的发育、分化和调节中具有关键功能。在这项研究中，发展了一种基于支持向量机的二肽组成预测细胞因子家族和子家族的方法。我们的方法所符合的细胞因子超家族的分类是在细胞因子家族cDNA数据库(DBCFC)中描述的，本研究中用于训练和测试的数据集是从DBCFC和蛋白质结构分类(SCOP)中获得的。通过7次交叉验证，该方法对细胞因子和非细胞因子的分类准确率为92.5%。该方法进一步能够预测七大类细胞因子，总体准确率为94.7%。在http://bioinfo.tsinghua.edu.cn/similar to huangni/CTKPred/建立了一个基于多类支持向量机的细胞因子识别和分类服务器。
Cell proliferation, differentiation and death are controlled by a multitude of cell-cell signals and loss of this control has devastating consequences. Prominent among these regulatory signals is the cytokine superfamily, which has crucial functions in the development, differentiation and regulation of immune cells. In this study, a support vector machine (SVM)-based method was developed for predicting families and subfamilies of cytokines using dipeptide composition. The taxonomy of the cytokine superfamily with which our method complies was described in the Cytokine Family cDNA Database (dbCFC) and the dataset used in this study for training and testing was obtained from the dbCFC and Structural Classification of Proteins (SCOP). The method classified cytokines and non-cytokines with an accuracy of 92.5% by 7-fold cross-validation. The method is further able to predict seven major classes of cytokine with an overall accuracy of 94.7%. A server for recognition and classification of cytokines based on multi-class SVMs has been set up at http://bioinfo.tsinghua.edu.cn/similar to huangni/CTKPred/.