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New Training Method for Computational Identification of Promoter

The computational promoter prediction has been based on specific genomic context that promoter has in large genomic sequence. A promoter finding system is trained with known promoter’s putative genomic context, find pattern, and search genomic sequence. However, the accuracy which genes can be predicted is still far from satisfactory: although, at the nucleotide level, 80% of genes are accurately predicted, at the exon level only 45% predicted, and at the whole-gene level only ∼20% [2]. To get better performance we focused on the diversity of promoters. Because using whole known promoter as one training set can neglect of diversity of promoters, it dulls the responsiveness of neural network. With our approach we clustered known promoters in binary tree and trained neural networks with each leaf node cluster.