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    An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies.

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    Authors
    Lancashire, Lee J
    Lemetre, Christophe
    Ball, Graham R
    Affiliation
    Clinical and Experimental Pharmacology, Paterson Institute for Cancer Research, University of Manchester, Manchester M20 4BX, UK. llancashire@picr.man.ac.uk
    Issue Date
    2009-05
    
    Metadata
    Show full item record
    Abstract
    Applications of genomic and proteomic technologies have seen a major increase, resulting in an explosion in the amount of highly dimensional and complex data being generated. Subsequently this has increased the effort by the bioinformatics community to develop novel computational approaches that allow for meaningful information to be extracted. This information must be of biological relevance and thus correlate to disease phenotypes of interest. Artificial neural networks are a form of machine learning from the field of artificial intelligence with proven pattern recognition capabilities and have been utilized in many areas of bioinformatics. This is due to their ability to cope with highly dimensional complex datasets such as those developed by protein mass spectrometry and DNA microarray experiments. As such, neural networks have been applied to problems such as disease classification and identification of biomarkers. This review introduces and describes the concepts related to neural networks, the advantages and caveats to their use, examples of their applications in mass spectrometry and microarray research (with a particular focus on cancer studies), and illustrations from recent literature showing where neural networks have performed well in comparison to other machine learning methods. This should form the necessary background knowledge and information enabling researchers with an interest in these methodologies, but not necessarily from a machine learning background, to apply the concepts to their own datasets, thus maximizing the information gain from these complex biological systems.
    Citation
    An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies. 2009, 10 (3):315-29 Brief. Bioinformatics
    Journal
    Briefings in Bioinformatics
    URI
    http://hdl.handle.net/10541/69776
    DOI
    10.1093/bib/bbp012
    PubMed ID
    19307287
    Type
    Article
    Language
    en
    ISSN
    1477-4054
    ae974a485f413a2113503eed53cd6c53
    10.1093/bib/bbp012
    Scopus Count
    Collections
    All Paterson Institute for Cancer Research
    Clinical and Experimental Pharmacology Group

    entitlement

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