Background: The scientific literature contains a wealth of information from different fields onpotential 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 mechanisticpathways between any two biomedical concepts.Results: Two case studies demonstrate the potential uses of MELODI and how it can generatehypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives theintermediate transcription factor SP1, recently confirmed to be physically interacting with ERG.Secondly, examining the relationship between a new potential risk factor for pancreatic canceridentifies possible mechanistic insights which can be studied in vitro.Conclusion: We have demonstrated the possible applications of MELODI, including two case studies.
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