Promoter prediction based on a multiple instance learning scheme

Xuefeng Zhou, Jianhua Ruan, Weixiong Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    2 Scopus citations

    Abstract

    Core promoters are crucial regions for initiation of gene transcription. Identification of core promoters is important to the understanding of transcriptional regulation and elucidation of relationships among genes of an organism. Experimentally locating core promoters is laborious and costly. Therefore, it is desirable to develop computational approaches to support and complement experimental methods. However, computational prediction of core promoters of eukaryotic species is challenging. In this paper, we first formulate the core promoter prediction problem as a variation of the multiple instance learning problem. We then develop a new algorithm for identifying core promoters with a high positive prediction rate and a high sensitivity. Since many computational biology problems can be formulated under the multiple instance learning paradigm, our approach may inspire future research of applying multiple instance learning techniques to complex biology problems and our method may have broad potential applications.

    Original languageEnglish (US)
    Title of host publication2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
    Pages295-301
    Number of pages7
    DOIs
    StatePublished - Oct 25 2010
    Event2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010 - Niagara Falls, NY, United States
    Duration: Aug 2 2010Aug 4 2010

    Publication series

    Name2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010

    Other

    Other2010 ACM International Conference on Bioinformatics and Computational Biology, ACM-BCB 2010
    CountryUnited States
    CityNiagara Falls, NY
    Period8/2/108/4/10

    ASJC Scopus subject areas

    • Biomedical Engineering
    • Health Information Management

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