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    MELODI: Mining Enriched Literature Objects to Derive Intermediates

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    Author
    Elsworth, B; Dawe, K; Vincent, EE; Langdon, R; Lynch, BM; Martin, RM; Relton, C; Higgins, JPT; Gaunt, TR
    Date
    2018-04-01
    Source Title
    International Journal of Epidemiology
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Lynch, Brigid
    Affiliation
    Melbourne School of Population and Global Health
    Metadata
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    Document Type
    Journal Article
    Citations
    Elsworth, B., Dawe, K., Vincent, E. E., Langdon, R., Lynch, B. M., Martin, R. M., Relton, C., Higgins, J. P. T. & Gaunt, T. R. (2018). MELODI: Mining Enriched Literature Objects to Derive Intermediates. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, 47 (2), pp.369-379. https://doi.org/10.1093/ije/dyx251.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255029
    DOI
    10.1093/ije/dyx251
    Abstract
    Background: The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing 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 prioritize 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. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro. Conclusions: We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].

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