Development of an effective machine-learning model for T-cell Mycobacterium tuberculosis (M. tuberculosis) epitopes is beneficial for saving biologist's time and effort for identifying epitope in a targeted antigen. Existing NetMHC 2.2, NetMHC 2.3, NetMHC 3.0 and NetMHC 4.0 estimate binding capacity of peptide. This is still a challenge for those servers to predict whether a given peptide is M. tuberculosis epitope or non-epitope. One of the servers, CTLpred, works in this category but it is limited to peptide length of 9-mers. Therefore, in this work direct method of predicting M. tuberculosis epitope or non-epitope has been proposed which also overcomes the limitations of above servers. The proposed method is able to work with variable length epitopes having size even greater than 9-mers. Identification of T-cell or B-cell epitopes in the targeted antigen is the main goal in designing epitope-based vaccine, immune-diagnostic tests and antibody production. Therefore, it is important to introduce a reliable system which may help in the diagnosis of M. tuberculosis. In the present study, computational intelligence methods are used to classify T-cell M. tuberculosis epitopes. The caret feature selection approach is used to find out the set of relevant features. The ensemble model is designed by combining three models and is used to predict M. tuberculosis epitopes of variable length (7-40-mers). The proposed ensemble model achieves 82.0% accuracy, 0.89 specificity, 0.77 sensitivity with repeated k-fold cross-validation having average accuracy of 80.61%. The proposed ensemble model has been validated and compared with NetMHC 2.3, NetMHC 4.0 servers and CTLpred T-cell prediction server.

中文翻译：

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预测T细胞结核分枝杆菌抗原决定簇的集成技术。

为T细胞结核分枝杆菌（M. tuberculosis）表位开发有效的机器学习模型，对于节省生物学家识别目标抗原表位的时间和精力是有益的。现有的NetMHC 2.2，NetMHC 2.3，NetMHC 3.0和NetMHC 4.0估计了肽的结合能力。对于那些服务器来说，预测给定的肽是结核分枝杆菌表位还是非表位仍然是一个挑战。其中一种服务器CTLpred可以在此类别中使用，但仅限于9-mers的肽段长度。因此，在这项工作中，已经提出了预测结核分枝杆菌表位或非表位的直接方法，这也克服了上述服务器的局限性。所提出的方法能够用于具有甚至大于​​9聚体的大小的可变长度表位。在设计基于表位的疫苗，免疫诊断测试和抗体生产中，鉴定目标抗原中的T细胞或B细胞表位是主要目标。因此，重要的是引入一种可靠的系统，该系统可以帮助诊断结核分枝杆菌。在本研究中，使用计算智能方法对T细胞结核分枝杆菌表位进行分类。插入号特征选择方法用于查找相关特征集。集合模型是通过组合三个模型而设计的，用于预测可变长度（7-40聚体）的结核分枝杆菌表位。所提出的集成模型实现了82.0％的准确度，0.89的特异性，0.77的灵敏度，并进行了重复的k倍交叉验证，平均准确度为80.61％。拟议的集成模型已经过验证，并与NetMHC 2.3进行了比较，