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mhcⅠ类结合预测的新机器学习方法
Novel Machine Learning Methods for MHC Class I Binding Prediction |
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| 课程网址: |
http://videolectures.net/prib2010_widmer_nmlm/
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| 主讲教师: |
Christian Widmer
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| 开课单位: |
图宾根大学
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| 开课时间: |
2010-10-14
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| 课程语种: |
英语
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| 中文简介: |
MHC I类分子是人体免疫系统的关键参与者。它们结合来自细胞内蛋白质的小肽并将它们呈递在细胞表面上以供免疫系统监测。预测这种MHC I类结合肽是基于肽的疫苗设计中的关键步骤，因此是计算免疫学中的主要问题之一。存在数千种不同类型的MHC I类分子，每种都显示出不同的结合特异性。大多数这些分子缺乏足够的训练数据阻碍了机器学习在这个问题中的应用。我们提出了两种方法来提高基于内核的机器学习方法对MHC I类绑定预测的预测能力：第一，修改加权度弦内核，允许掺入氨基酸特性。其次，我们提出了一个增强的多任务内核和一个优化程序来微调内核参数。两种方法的结合可以提高性能，我们在IEDB基准数据集上进行了演示。
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| 课程简介: |
MHC class I molecules are key players in the human immune system. They bind small peptides derived from intracellular proteins and present them on the cell surface for surveillance by the immune system. Prediction of such MHC class I binding peptides is a vital step in the design of peptide-based vaccines and therefore one of the major problems in computational immunology. Thousands of different types of MHC class I molecules exist, each displaying a distinct binding specificity. The lack of sufficient training data for the majority of these molecules hinders the application of Machine Learning to this problem. We propose two approaches to improve the predictive power of kernel-based Machine Learning methods for MHC class I binding prediction: First, a modification of the Weighted Degree string kernel that allows for the incorporation of amino acid properties. Second, we propose an enhanced Multitask kernel and an optimization procedure to fine-tune the kernel parameters. The combination of both approaches yields improved performance, which we demonstrate on the IEDB benchmark data set.
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| 关 键 词: |
人体免疫系统; 免疫学; 内核
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-14：lxf
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| 阅读次数: |
60
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