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当前位置: 首页 > 文章 > 基于K-mer-SVM的piRNA预测 福建农林大学学报(自然科学版) 2016,45 (2) 228-231
Position: Home > Articles > piRNA prediction based on K-mer-SVM method Journal of Fujian Agriculture and Forestry University(Natural Science Edition) 2016,45 (2) 228-231

基于K-mer-SVM的piRNA预测

作  者：
李小林;黄世国
单  位：
福建农林大学计算机与信息学院
关键词：
支持向量机;piRNA;K-mer;分类
摘  要：
采用支持向量机(SVM)结合K-mer分布特征预测piRNA.利用多种生物的非编码RNA序列数据库,从中挑选出piRNA序列作为正样本,并以由该数据库构建的非piRNA序列作为负样本,将正样本和负样本构成的数据随机取出50%作为训练集,将剩余的数据作为测试集;提取正样本和负样本序列的K-mer分布特征构建特征矩阵;用SVM对其进行分类,实现piRNA预测.结果表明K-mer-SVM在准确率、正例覆盖率、MCC和F测度等分类指标上均明显优于K-mer-LDA,说明K-merSVM是更好的piRNA预测算法.
译  名：
piRNA prediction based on K-mer-SVM method
作  者：
LI Xiaolin;HUANG Shiguo;College of Computer and Information,Fujian Agriculture and Forestry University;
关键词：
support vector machine;;piRNA;;K-mer;;classification
摘  要：
To alleviate identification problems attributed to divergent structure and low conservative of piRNA,combined method based on support vector machine( SVM) and K-mer was applied to predict sequence of piRNA. To start with,piRNA sequences from non-coding RNA database of several species were identified as positive sample,and from the same database non-piRNA sequences were set as negative sample. Positive and negative samples were reconstituted as a new dataset,half of which were randomized as training dataset and the remaining as testing dataset. Subsequently,feature matrix was derived from K-mer distribution which was based on positive and negative sequences,and followed by being classified by SVM and piRNA prediction. Results revealed that K-mer-SVM had higher accuracy,sensitivity and specificity,and better classifying performance of MCC and F-meature than those of K-mer-LDA. In other word,K-mer-SVM classifier is likely to be a better algorithm for piRNA prediction.

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