NDLI: Human Promoter Recognition using Kullback-Leibler Divergence

Content Provider	IEEE Xplore Digital Library
Author	Jia Zeng Xiao-Qin Cao Hong Yan
Copyright Year	2007
Description	Author affiliation: City Univ. of Hong Kong, Kowloon (Jia Zeng; Xiao-Qin Cao; Hong Yan)
Abstract	Human gene expression can be regulated at four levels: differential gene transcription, selective nuclear RNA (nRNA) processing, selective messenger RNA (mRNA) translation, and differential protein modification. In the transcription level, the promoter region plays an important role in binding of RNA polymerase for the subsequent initiation of transcription. In this paper, we use the Kullback-Leibler (KL) divergence to select the most informative and discriminative word-based features to differentiate the promoter and non-promoter regions in large genomic sequences. First, we assume that the nucleotide sequence is composed of random "words" with the same length. Second, based on promoter and non-promoter training samples, we have two discrete distributions over the same random word, and the KL divergence is a measure of the distance between these distributions. Finally, we select two groups of words, one for the class "promoter" and the other for the class "non-promoter", which have the maximum KL divergence in training samples. The gene recognition proceeds by comparing the number of word matches between the two groups of informative words. Encouraging results are obtained in experiments on the human promoter recognition in DBTSS database. The recognition rate is comparable with those of the state-of-the-art promoter recognizers. Future works about the combination of words with different lengths are also discussed.
Starting Page	3319
Ending Page	3325
File Size	874640
Page Count	7
File Format	PDF
ISBN	9781424409723
DOI	10.1109/ICMLC.2007.4370721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans RNA DNA Gene expression Polymers Sequences Proteins Genomics Bioinformatics Machine learning
Content Type	Text
Resource Type	Article

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