BCESCP:基于多类支持向量机的B细胞表位亚类分类研究（英文）

BCESCP: Discrimination and Prediction of Subclasses of B-cell Epitopes Using*Multi-class Support Vector Machine

目的:根据诱导的特异性抗体种型,B细胞表位被分成不同的亚类。探索表位多类亚类之间的区别非常重要,能促进揭示免疫系统为什么会针对不同的表位产生特异性抗体应答。基于多类支持向量机,发展一个能区分多类表位亚类并且能预测B细胞表位的亚类类别的模型。方法:训练模型的数据集来源于免疫表位数据库,数据集包含4类数据,对应4种B细胞表位亚类:Ig A表位,Ig E表位,Ig G表位以及Ig M表位。通过5折交叉验证,分别探索氨基酸组成特征,quasi-序列顺序特征以及二肽组成特征区分表位多类亚类的能力。结果:实验结果表明二肽组成特征的区分性能最好,整体准确率为61.58%,应用此多类分类模型,开发了一个名为BCESCP的免费使用的B细胞表位的亚类类别预测服务器,BCESCP可以通过如下地址访问:http://www.bioinfo.tsinghua.edu.cn/epitope/BCESCP/。

Objective: B-cell epitopes( BCEs) can be divided into subclasses according to the isotype of antibody( Ab) that BCEs can induce. It is important to discriminate among these BCEs subclasses because it would be beneficial to understand why the immune system produces different Ab isotypes against different BCEs. Based on a multi-class support vector machine( SVM),models have been developed to discriminate among BCEs subclasses. Methods: A fourclass dataset including four BCEs subclasses was created to train and test the models. Various primary sequence features including amino acid composition,quasi-sequence-order and dipeptide composition have been computed for comparing their ability to discriminate BCEs subclasses using five-fold cross validation. Results: It was observed that dipeptide composition based model achieved the highest overall accuracy of 61. 58%. Moreover,a web server,BCESCP,of the best performing multi-class classification model has been provided for predicting subclass type of BCEs. BCESCP is freely available at http: / / www. bioinfo. tsinghua. edu. cn / epitope / BCESCP /.