| 基于支持向量机的酵母重组热点和冷点的预测 |
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| 引用本文： | 翁建洪,周童,孙啸,陆祖宏.基于支持向量机的酵母重组热点和冷点的预测[J].东南大学学报,2006,22(1):112-116. |
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| 作者姓名： | 翁建洪 周童 孙啸 陆祖宏 |
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| 作者单位： | 东南大学生物电子学国家重点实验室 南京210096 |
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| 摘 要： | 使用基于统计学习理论的支持向量机(SVM)方法,提出了针对重组热点和冷点分类预测的新方法.对酵母基因组的303个重组热点开放阅读框(hot ORF)以及48个重组冷点开放阅读框(cold ORF),提取了序列的一般二联碱基丰度特征,以及基于密码子使用偏性的二联碱基丰度特征,然后使用二倍交叉验证方法,选择不同的核函数和对应参数,对数据集进行了训练和分类预测.研究结果表明,当使用径向基核函数,并采用基于密码子使用偏性的二联碱基丰度特征时,预测准确率为87·47%.
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| 关 键 词： | 减数分裂重组 热点 冷点 二联碱基丰度 支持向量机 |
| 收稿时间： | 06 16 2005 12:00AM |
Support vector machine for prediction of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae |
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| Weng Jianhong,Zhou Tong,Sun Xiao,Lu Zuhong.Support vector machine for prediction of meiotic recombination hotspots and coldspots in Saccharomyces cerevisiae[J].Journal of Southeast University(English Edition),2006,22(1):112-116. |
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| Authors: | Weng Jianhong Zhou Tong Sun Xiao Lu Zuhong |
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| Institution: | State Key Laboratory of Bioelectronics, Southeast University, Nanjing 210096, China |
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| Abstract: | A novel method for predicting hotspots and coldspots using support vector machine (SVM) based on statistical learning theory is developed. This method is applied to published 303 hot and 48 cold open reading frames (ORFs) in Saccharomyces cerevisiae. The sequence features of general dinucleotide abundance and dinucleotide abundance based on codon usage are extracted, and then the data sets are classified with different parameters and kernel functions combined with the method of two-fold cross validation. The result indicates that 87.47% accuracy can be reached when classifying hot and cold ORF sequences with the kernel of radial basis function combined with dinucleotide abundance based on codon usage. |
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| Keywords: | meiotic recombination hotspot coldspot dinucleotide abundance support vector machine |
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