Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21029
Title: Promoter Explorer : an effective promoter identification method based on the AdaBoost algorithm
Authors: Xie, X
Wu, S
Lam, KM 
Yan, H
Issue Date: 2006
Publisher: Oxford University Press
Source: Bioinformatics, 2006, v. 22, no. 22, p. 2722-2728 How to cite?
Journal: Bioinformatics 
Abstract: Motivation: Promoter prediction is important for the analysis of gene regulations. Although a number of promoter prediction algorithms have been reported in literature, significant improvement in prediction accuracy remains a challenge. In this paper, an effective promoter identification algorithm, which is called PromoterExplorer, is proposed. In our approach, we analyze the different roles of various features, that is, local distribution of pentamers, positional CpG island features and digitized DNA sequence, and then combine them to build a high-dimensional input vector. A cascade AdaBoost-based learning procedure is adopted to select the most 'informative' or 'discriminating' features to build a sequence of weak classifiers, which are combined to form a strong classifier so as to achieve a better performance. The cascade structure used for identification can also reduce the false positive. Results: PromoterExplorer is tested based on large-scale DNA sequences from different databases, including the EPD, DBTSS, GenBank and human chromosome 22. Experimental results show that consistent and promising performance can be achieved.
URI: http://hdl.handle.net/10397/21029
ISSN: 1367-4803
EISSN: 1460-2059
DOI: 10.1093/bioinformatics/btl482
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