An effective promoter detection method using the AdaBoost algorithm

Xudong Xie, Shuanhu Wu, Kin Man Lam, Hong Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


In this paper, an effective promoter detection algorithm, which is called PromoterExplorer, is proposed. In our approach, various features, i.e. local distribution of pentamers, positional CpG island features and digitized DNA sequence, are combined 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. A number of weak classifiers construct a strong classifier, which can achieve a better performance. In order to reduce the false positive, a cascade structure is used for detection. PromoterExplorer is tested based on large-scale DNA sequences from different databases, including EPD, Genbank and human chromosome 22. The proposed method consistently outperforms PromoterInspector and Dragon Promoter Finder.
Original languageEnglish
Title of host publicationProceedings of the 5th Asia-Pacific Bioinformatics Conference, APBC 2007
Number of pages10
Publication statusPublished - 1 Dec 2007
Event5th Asia-Pacific Bioinformatics Conference, APBC 2007 - Hong Kong, Hong Kong
Duration: 15 Jan 200717 Jan 2007


Conference5th Asia-Pacific Bioinformatics Conference, APBC 2007
Country/TerritoryHong Kong
CityHong Kong

ASJC Scopus subject areas

  • Bioengineering
  • Information Systems

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