关键词: 启动子, 启动子识别, 主成分分析, CpG岛, 反向传播（BP）神经网络

Abstract: In order to improve the situation of high false positive in eukaryotic promoter recognition, a promoter predication method based on integrate-features is presented. The method employs human promoter codon and pentamer information as features, and extracts principal components by PCA. The 10-dimension features and 2-dimension CpG island information, based on integrated features, are put into the BP neural network as classifier. The experimental results show that the method not only reduces the false positives but also obtains a higher sensitivity and specificity.

Key words: promoter, promoter recognition, principal component analysis, CpG island, Back Propagation（BP） neural network