Khanna Divya, Rana Prashant Singh
Computer Science and Engineering Department, Thapar University, Patiala, Punjab 147004, India.
Computer Science and Engineering Department, Thapar University, Patiala, Punjab 147004, India.
Immunol Lett. 2017 Apr;184:51-60. doi: 10.1016/j.imlet.2017.01.017. Epub 2017 Feb 16.
Identification of antigen for inducing specific class of antibody is prime objective in peptide based vaccine designs, immunodiagnosis, and antibody productions. It's urge to introduce a reliable system with high accuracy and efficiency for prediction. In the present study, a novel multilevel ensemble model is developed for prediction of antibodies IgG and IgA. Epitope length is important in training the model and it is efficient to use variable length of epitopes. In this ensemble approach, seven different machine learning models are combined to predict variable length of epitopes (4 to 50). The proposed model of IgG specific epitopes achieves 94.43% of accuracy and IgA specific epitopes achieves 97.56% of accuracy with repeated 10-fold cross validation. The proposed model is compared with the existing system i.e. IgPred model and outcome of proposed model is improved.
鉴定用于诱导特定类别抗体的抗原是基于肽的疫苗设计、免疫诊断和抗体生产中的主要目标。迫切需要引入一种具有高精度和高效率的可靠预测系统。在本研究中，开发了一种新型的多级集成模型来预测抗体IgG和IgA。表位长度在训练模型中很重要，使用可变长度的表位是有效的。在这种集成方法中，七种不同的机器学习模型被组合起来预测可变长度的表位（4到50）。所提出的IgG特异性表位模型在重复10折交叉验证时达到了94.43%的准确率，IgA特异性表位模型达到了97.56%的准确率。将所提出的模型与现有系统即IgPred模型进行了比较，所提出模型的结果得到了改进。
