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    Ensemble approach combining multiple methods improves human transcription start site prediction.

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    Authors
    Dineen, David G
    Schröder, Markus
    Higgins, Desmond G
    Cunningham, Pádraig
    Affiliation
    Complex and Adaptive Systems Laboratory (CASL), University College Dublin, Belfield, Dublin 4, Ireland. david.dineen@ucd.ie
    Issue Date
    2010
    MeSH
    Base Pairing
    Computational Biology
    Genome, Human
    Humans
    Principal Component Analysis
    Promoter Regions, Genetic
    Software
    Transcription Initiation Site
    
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    Citation
    Ensemble approach combining multiple methods improves human transcription start site prediction. 2010, 11:677 BMC Genomics
    Journal
    BMC genomics
    URI
    http://hdl.handle.net/10147/125892
    DOI
    10.1186/1471-2164-11-677
    PubMed ID
    21118509
    Abstract
    The computational prediction of transcription start sites is an important unsolved problem. Some recent progress has been made, but many promoters, particularly those not associated with CpG islands, are still difficult to locate using current methods. These methods use different features and training sets, along with a variety of machine learning techniques and result in different prediction sets.
    We demonstrate the heterogeneity of current prediction sets, and take advantage of this heterogeneity to construct a two-level classifier ('Profisi Ensemble') using predictions from 7 programs, along with 2 other data sources. Support vector machines using 'full' and 'reduced' data sets are combined in an either/or approach. We achieve a 14% increase in performance over the current state-of-the-art, as benchmarked by a third-party tool.
    Supervised learning methods are a useful way to combine predictions from diverse sources.
    Item Type
    Article
    Language
    en
    ISSN
    1471-2164
    ae974a485f413a2113503eed53cd6c53
    10.1186/1471-2164-11-677
    Scopus Count
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