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| 贝叶斯方法在蛋白质耐热性分类中的研究 | |
| 引用本文： | 丁彦蕊,蔡宇杰,孙俊,须文波.贝叶斯方法在蛋白质耐热性分类中的研究[J].微计算机信息,2007,23(7):308-310. |
| 作者姓名： | 丁彦蕊 蔡宇杰 孙俊 须文波 |
| 作者单位： | 1. 214122,江苏无锡,江南大学,信息工程学院;214036,江苏无锡,江南大学,工业生物技术教育部重点实验室 2. 214036,江南,江南大学,生物工程学院 3. 214122,江苏无锡,江南大学,信息工程学院 |
| 摘 要： | 氨基酸含量是影响蛋白质耐热性的主要因素。本文以氨基酸含量为特征向量,研究了贝叶斯方法预测蛋白质耐热性的准确度。结果表明,基于贝叶斯方法的局部预测率和全局预测率分别为73.1%和76.1%。这不仅表明贝叶斯方法适合于蛋白质耐热性的分类,而且也证明了氨基酸含量的确对蛋白质耐热性有重要作用。 |
| 关 键 词： | 氨基酸含量 贝叶斯 蛋白质耐热性 |
| 文章编号： | 1008-0570（2007）03-1-0308-03 |
| 修稿时间： | 2007-01-12 |
Classification of protein thermostability using Bayes methods |
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| DING YANRUI,CAI YUJIE,SUN JUN,XU WENBO.Classification of protein thermostability using Bayes methods[J].Control & Automation,2007,23(7):308-310. | |
| Authors: | DING YANRUI CAI YUJIE SUN JUN XU WENBO |
| Affiliation: | 1.School of information technology, Southern Yangtz University, Jiangsu Wuxi 214122, China;2.Key Laboratory of Industrial Biotechnology,Jiangsu Wuxi 214036, China;3.School of biotechnology,Jiangsu Wuxi 214036, China |
| Abstract: | Amino acid composition is the most important factor that influences the protein thermostability. Regarding amino acid com- position as eigenvector, protein thermostability was classified based on Bayes methods. It was found that the local accuracy and global accuracy are 73.1% and 76.1% respectively using Bayes methods. The results not only show that Bayes methods is suitable for pre- dicting protein thermostability, but also prove the conclusion that amino acid composition severely influence protein thermostability. |
| Keywords: | amino acid composition Bayes protein thermostability |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |