| 支持向量分类器及其在原核生物基因计算识别中的应用 |
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| 引用本文： | 黄国华.支持向量分类器及其在原核生物基因计算识别中的应用[J].湖南第一师范学报,2011,11(2):133-136. |
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| 作者姓名： | 黄国华 |
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| 作者单位： | 邵阳学院,理学与信息科学系,湖南,邵阳,422000 |
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| 摘 要： | 以支持向量机为分类器，序列的k-letter词为特征，建立了原核生物的基因识别模型。分别选取已知功能的基因为正样本，和与等长正样本的随机突变序列为负样本组成训练集。5倍交叉实验的结果表示，对于具有不同核函数的支持向量机以及不同长度的词特征，其预测准确率不同，最高的可达94％以上，最差的低于60％；长度为3的词的特征的分类结果最好，其次是长度为4。这说明3联核苷酸为基因序列比较好的统计特征。
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| 关 键 词： | 支持向量机 基因识别 核函数 K—letter词 |
Support Vector Machine and Its Application in Computational Recognition of Prokaryotes Gene |
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| HUANG Guo-hua.Support Vector Machine and Its Application in Computational Recognition of Prokaryotes Gene[J].Journal of First Teachers College of Hunan,2011,11(2):133-136. |
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| Authors: | HUANG Guo-hua |
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| Institution: | HUANG Guo-hua ( Department of Science and Information-science, Shaoyang University, Shaoyang, Hunan 422000) |
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| Abstract: | A model of gene recognition of Prokaryotes is built, with Support Vector Machine as a method of classification and k-letters word of a sequence as a characteristic. The train set consists of positive samples which are chosen out from known-function genes and equal negative ones generated randomly from the corresponding positive sample. The resuk in 5-cross experiments indicates that accuracy of prediction for SVMs varies with kernal functions and length of word, better above 94% and worse below 60%; the best classification result is of 3-letter word and next 4-letter word. This demonstrates 3 amino acids is a better statistical characteristic ofgene sequences. |
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| Keywords: | Support Vector Machine gene recognition kemal function K-letter word |
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