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MELODI - Mining Enriched Literature Objects to Derive Intermediates

View ORCID ProfileBenjamin Elsworth, Karen Dawe, Emma E Vincent, Ryan Langdon, Brigid M Lynch, Richard M Martin, Caroline Relton, Julian Higgins, Tom Gaunt
doi: https://doi.org/10.1101/118513
Benjamin Elsworth
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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  • ORCID record for Benjamin Elsworth
Karen Dawe
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Emma E Vincent
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Ryan Langdon
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Brigid M Lynch
2Cancer Epidemiology and Intelligence Division, Cancer Council Victoria, Melbourne, Victoria, Australia
3Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Victoria, Australia
4Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
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Richard M Martin
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Caroline Relton
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Julian Higgins
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Tom Gaunt
1MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom
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Abstract

Motivation The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, 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.

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

Availability MELODI has been implemented as a Python/Django web application, and is freely available to use at www.melodi.biocompute.org.uk

Contact melodi{at}biocompute.org.uk

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 20, 2017.
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MELODI - Mining Enriched Literature Objects to Derive Intermediates
Benjamin Elsworth, Karen Dawe, Emma E Vincent, Ryan Langdon, Brigid M Lynch, Richard M Martin, Caroline Relton, Julian Higgins, Tom Gaunt
bioRxiv 118513; doi: https://doi.org/10.1101/118513
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MELODI - Mining Enriched Literature Objects to Derive Intermediates
Benjamin Elsworth, Karen Dawe, Emma E Vincent, Ryan Langdon, Brigid M Lynch, Richard M Martin, Caroline Relton, Julian Higgins, Tom Gaunt
bioRxiv 118513; doi: https://doi.org/10.1101/118513

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