Prediction of MHC class II binding peptides based on an iterative learning model.
Prediction of MHC class II binding peptides based on an iterative learning model.
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DOI：
10.1186/1745-7580-1-6
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发表时间：
2005-12-13
期刊：
影响因子：
--
通讯作者：
Dai Y
中科院分区：
文献类型：
--
作者：
Murugan N;Dai Y
预测抗原肽与主要组织相容性复合体（MHC）II类分子的结合能力在疫苗开发中是重要的。每个结合肽的可变长度使这种预测复杂化。出于文本挖掘模型的设计用于从标记和未标记的例子建立一个分类器，我们已经开发了一个迭代的监督学习模型的预测MHC II类结合肽。线性规划（LP）模型在每次迭代的学习任务，因为它是快速的，可以重新优化前一个分类器时，训练集被改变。新模型的性能进行了评估与基准数据集。结果表明，该模型实现了预测的准确性，是竞争力相比，先进的预测（吉布斯采样器和TEPITOPE）。从我们模型的一个变体获得的原始和同源性降低的基准集的ROC曲线下的平均面积分别为0.753和0.715。吉布斯取样器的相应值分别为0.744和0.673，TEPITOPE的相应值分别为0.702和0.667。迭代学习程序似乎是有效的预测MHC II类结合剂。它为这个重要的预测问题提供了另一种方法。
Prediction of the binding ability of antigen peptides to major histocompatibility complex (MHC) class II molecules is important in vaccine development. The variable length of each binding peptide complicates this prediction. Motivated by a text mining model designed for building a classifier from labeled and unlabeled examples, we have developed an iterative supervised learning model for the prediction of MHC class II binding peptides. A linear programming (LP) model was employed for the learning task at each iteration, since it is fast and can re-optimize the previous classifier when the training sets are altered. The performance of the new model has been evaluated with benchmark datasets. The outcome demonstrates that the model achieves an accuracy of prediction that is competitive compared to the advanced predictors (the Gibbs sampler and TEPITOPE). The average areas under the ROC curve obtained from one variant of our model are 0.753 and 0.715 for the original and homology reduced benchmark sets, respectively. The corresponding values are respectively 0.744 and 0.673 for the Gibbs sampler and 0.702 and 0.667 for TEPITOPE. The iterative learning procedure appears to be effective in prediction of MHC class II binders. It offers an alternative approach to this important predictionproblem.