• Daniel G Jamieson, Andrew Moss, Michael Kennedy, Sherrie Jones, Goran Nenadic, David L Robertson, and Ben Sidders.
• Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK; Computer Science, Faculty of Engineering and Physical Sciences, University of Manchester, Manchester, UK.
• Pain. 2014 Nov 1; 155 (11): 224322522243-52.

AbstractUnderstanding the molecular mechanisms associated with disease is a central goal of modern medical research. As such, many thousands of experiments have been published that detail individual molecular events that contribute to a disease. Here we use a semi-automated text mining approach to accurately and exhaustively curate the primary literature for chronic pain states. In so doing, we create a comprehensive network of 1,002 contextualized protein-protein interactions (PPIs) specifically associated with pain. The PPIs form a highly interconnected and coherent structure, and the resulting network provides an alternative to those derived from connecting genes associated with pain using interactions that have not been shown to occur in a painful state. We exploit the contextual data associated with our interactions to analyse subnetworks specific to inflammatory and neuropathic pain, and to various anatomical regions. Here, we identify potential targets for further study and several drug-repurposing opportunities. Finally, the network provides a framework for the interpretation of new data within the field of pain.Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

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