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基于粒子群优化的支持向量机算法识别人类基因启动子
张文,陈园园,张瑾,骈聪,李琴,张良云
0
(南京农业大学理学院,南京 210095)
摘要:
人类基因启动子识别是医学研究的基本需要。提取DNA序列碱基的PZ曲线特征、二核苷酸空间结构特征、保守信号似然得分,以及K联体似然得分,结合GC含量变化和非均匀指数,构建基于粒子群优化的支持向量机算法来识别人类基因启动子。利用粒子群优化支持向量机参数进行优化避免了人为选择的随机性,并且在分类问题中表现出较好的稳健性。对测试集的10-折交叉检验结果为:敏感性为92%,特异性为91%,马修斯关联系数为0.83。该结果表明,基于粒子群优化的支持向量机算法能有效识别启动子序列。
关键词:  相位特异PZ曲线  粒子群优化  支持向量机  启动子预测
DOI:10.13610/j.cnki.1672-352x.20150302.024
投稿时间:2014-10-22
基金项目:教育部博士点基金(20100097110040), 中央高校基本科研业务费专项资金(KYZ201125)和江苏省自然科学
Recognition of gene promoters in human beings based on the particle swarm optimized support vector machine algorithm
ZHANG Wen,CHEN Yuanyuan,ZHANG Jin,PIAN Cong,LI Qin,ZHANG Liangyun
(College of Science, Nanjing Agricultural University, Nanjing 210095)
Abstract:
Recognition of gene promoters in human beings is a basic requirement for medical research. It was achieved through analysis of phase-specific PZ curves of nucleotide, spatial structure of nucleotide, conservative signal and K-mer likelihood score in DNA sequence, as well as GC content changes and in-homogeneity index. The support vector machine algorithm based-particle swarm optimization was proposed to identify human gene promoters. Using PSO algorithm to optimize the parameters of SVM can avoid the randomness of artificial selection and present better robustness in classification. The sensitivity, specificity and MCC tested by the 10-fold cross-validation were 92%, 91%, and 0.83, respectively. The result indicated that PSO-SVM method can be used to effectively identify promoter sequences.
Key words:  phase-specific PZ curve  particle swarm optimization (PSO)  support vector machine (SVM)  promoter prediction

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