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Publication - Dr Tom Gaunt

    MELODI - Mining Enriched Literature Objects to Derive Intermediates

    Citation

    Elsworth, B, Dawe, K, Vincent, E, Langdon, R, Lynch, B, Martin, R, Relton, C, Higgins, J & Gaunt, T, 2017, ‘MELODI - Mining Enriched Literature Objects to Derive Intermediates’. International Journal of Epidemiology.

    Abstract

    Background: The scientific literature contains a wealth of information from different fields on
    potential disease mechanisms. However, identifying and prioritising mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritise mechanisms for more focused and detailed analysis.

    Methods: Here we present MELODI, a literature mining platform that can identify mechanistic
    pathways between any two biomedical concepts.

    Results: Two case studies demonstrate the potential uses of MELODI and how it can generate
    hypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives the
    intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG.
    Secondly, examining the relationship between a new potential risk factor for pancreatic cancer
    identifies possible mechanistic insights which can be studied in vitro.

    Conclusion: We have demonstrated the possible applications of MELODI, including two case studies.

    Full details in the University publications repository