El-Manzalawy Yasser, Dobbs Drena, Honavar Vasant
Artificial Intelligence Laboratory, Iowa State University, Ames, IA 50010, USA.
Comput Syst Bioinformatics Conf. 2008;7:121-32.
Identifying B-cell epitopes play an important role in vaccine design, immunodiagnostic tests, and antibody production. Therefore, computational tools for reliably predicting B-cell epitopes are highly desirable. We explore two machine learning approaches for predicting flexible length linear B-cell epitopes. The first approach utilizes four sequence kernels for determining a similarity score between any arbitrary pair of variable length sequences. The second approach utilizes four different methods of mapping a variable length sequence into a fixed length feature vector. Based on our empirical comparisons, we propose FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel. Our results demonstrate that FBCPred significantly outperforms all other classifiers evaluated in this study. An implementation of FBCPred and the datasets used in this study are publicly available through our linear B-cell epitope prediction server, BCPREDS, at: http://ailab.cs.iastate.edu/bcpreds/.
识别B细胞表位在疫苗设计、免疫诊断测试和抗体生产中发挥着重要作用。因此，非常需要能够可靠预测B细胞表位的计算工具。我们探索了两种用于预测可变长度线性B细胞表位的机器学习方法。第一种方法利用四个序列核来确定任意一对可变长度序列之间的相似性得分。第二种方法利用四种不同的方法将可变长度序列映射到固定长度的特征向量中。基于我们的实证比较，我们提出了FBCPred，这是一种使用子序列核预测可变长度线性B细胞表位的新方法。我们的结果表明，FBCPred显著优于本研究中评估的所有其他分类器。通过我们的线性B细胞表位预测服务器BCPREDS（网址：http://ailab.cs.iastate.edu/bcpreds/）可公开获得FBCPred的实现以及本研究中使用的数据集。
