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In spite of the recent development of computational methods for human promoter prediction, the prediction performance still needs improvement. In particular, the high false positive rate of the traditional approaches decreases the prediction reliability and leads to erroneous results in gene annotation. To improve the prediction accuracy and reliability, a DNA numerical representation and neural network...
Promoter recognition in various organisms is an area of interest in bioinformatics. In this paper, a feed-forward neural network classifier is presented to predict promoters in three organisms using a DNA numerical representation approach. The proposed system was found to be able to predict promoters with a sensitivity of 87%, 87%, 99% while reducing false prediction rate for non-promoter sequences...
DNA sequence analysis using digital signal processing requires conversion of a base sequence to a numerical sequence. The choice of the numerical representation of a DNA sequence affects how well its biological properties can be reflected in the numerical domain for the detection and identification of the characteristics of special regions of interest. This paper presents some selected methods of...
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