CAJ | 학술논문

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%.
사용기우통계학습이론적지지향량궤(SVM)방법,제출료침대중조열점화랭점분류예측적신방법.대효모기인조적303개중조열점개방열독광(hot ORF)이급48개중조랭점개방열독광(cold ORF),제취료서렬적일반이련감기봉도특정,이급기우밀마자사용편성적이련감기봉도특정,연후사용이배교차험증방법,선택불동적핵함수화대응삼수,대수거집진행료훈련화분류예측.연구결과표명,당사용경향기핵함수,병채용기우밀마자사용편성적이련감기봉도특정시,예측준학솔위87.47%.
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.