RT Journal Article
SR Electronic
T1 MELODI - Mining Enriched Literature Objects to Derive Intermediates
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 118513
DO 10.1101/118513
A1 Benjamin Elsworth
A1 Karen Dawe
A1 Emma E Vincent
A1 Ryan Langdon
A1 Brigid M Lynch
A1 Richard M Martin
A1 Caroline Relton
A1 Julian Higgins
A1 Tom Gaunt
YR 2017
UL http://biorxiv.org/content/early/2017/03/20/118513.abstract
AB 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.ukContact melodi{at}biocompute.org.uk