Prediction of CTL epitopes using QM, SVM and ANN techniques
Prediction of CTL epitopes using QM, SVM and ANN techniques
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DOI：
10.1016/j.vaccine.2004.02.005
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发表时间：
2004-08-13
期刊：
影响因子：
5.5
通讯作者：
Raghava, GPS
中科院分区：
文献类型：
--
作者：
Bhasin, M;Raghava, GPS
细胞毒性T淋巴细胞(CTL)表位是各种疾病亚单位疫苗设计的潜在候选者。现有的T细胞表位预测方法大多是间接预测MHC-I类结合，而不是CTL表位。在这项研究中，系统地尝试开发一种直接从抗原序列预测CTL表位的方法。该方法基于定量矩阵和机器学习技术，如支持向量机和人工神经网络。该方法已经在T细胞表位和非表位的非冗余数据集上进行了训练和测试，其中包括1137个实验证实的MHC I类限制性T细胞表位。QM方法、ANN方法和支持向量机方法的准确率分别为70.0%、72.2%和75.2%。这些方法的性能已经通过在灵敏度和特异度几乎相等的分界点上的留一交叉验证(LOOCV)进行了评估。最后，使用两种机器学习方法对CTL表位进行共识预测和组合预测。这些方法的性能在盲数据集上进行了评估，其中基于机器学习的方法比基于QM的方法性能更好。我们还通过亚群分析证明了我们的方法可以区分T细胞表位和MHC结合(非表位)。简而言之，该方法允许使用QM、支持向量机、神经网络方法预测CTL表位。该方法还有助于预测预测的T细胞表位中的MHC限制。该方法可在以下网址获得。Http://www.imtech.res.in/raghava/ctlpred/.(C)2004爱思唯尔有限公司。保留所有权利。
Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV)at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes. The method is available at. http://www.imtech.res.in/raghava/ctlpred/. (C) 2004 Elsevier Ltd. All rights reserved.