MELODI: Mining Enriched Literature Objects to Derive Intermediates | Zendy

Benjamin Elsworth | Zendy; Karen Dawe | Zendy; Emma E. Vincent | Zendy; Ryan Langdon | Zendy; Brigid M. Lynch | Zendy; Richard M. Martin | Zendy; Caroline L. Relton | Zendy; Julian P. T. Higgins | Zendy; Tom R. Gaunt | Zendy

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MELODI: Mining Enriched Literature Objects to Derive Intermediates

Author(s) -

Benjamin Elsworth,

Karen Dawe,

Emma E. Vincent,

Ryan Langdon,

Brigid M. Lynch,

Richard M. Martin,

Caroline L. Relton,

Julian P. T. Higgins,

Tom R. Gaunt

Publication year - 2018

Publication title -

international journal of epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.406

H-Index - 208

eISSN - 1464-3685

pISSN - 0300-5771

DOI - 10.1093/ije/dyx251

Subject(s) - python (programming language) , computer science , data science , scientific literature , prostate cancer , computational biology , data mining , bioinformatics , information retrieval , biology , cancer , genetics , programming language , paleontology

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.

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