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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
ABSTRACT - Internet Electron. J. Mol. Des. August 2003, Volume 2, Number 8, 527-538

Artificial Neural Network Method for Predicting Protein Coding Genes in the Yeast Genome
Chun Li, Ping-an He, and Jun Wang
Internet Electron. J. Mol. Des. 2003, 2, 527-538

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Abstract:
The rapid growth of DNA sequences data in various DNA databanks has made analyzing these sequences, especially, finding genes in them very important, and it is even a more critical task at present to clarify the number of genes. The motivation of this paper is to suggest an artificial neural network method specific for predicting protein-coding genes in the yeast genome. We first obtain a 12-dimensional vector from a DNA primary sequence, and then construct a 12×21×1 three-layer feedforward neural network. After being trained in a supervised manner with the error back-propagation algorithm by sufficient samples, the network is examined by the cross-validation test. As a result, the average absolute error δ and the average variance σ2 are 0.0084 and 0.0077, respectively, and the accuracy of the prediction is better than 96%. Based on this, it was found that the numbers of coding ORFs in the 2nd-6th classes are 393, 189, 803, 924 and 229, respectively. Thus, the total number of protein coding genes in the yeast genome is equal to 5930 coincident with a widely accepted range 5800-6000. The names of putative non-coding ORFs are listed in detail. The results imply that the current artificial neural network method is a useful computer technique for predicting protein-coding genes, and can be extended to find genes with more complicated structures.

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