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Archive workshop and seminar slides Professor Peter Karmerman…
Wellcome Trust conference The Challenges of Chronic Pain

11-13 March 2015
Wellcome Trust Genome Campus, Hinxton, Cambridge, UK

Abstract deadline: 30 January 2015
Registration deadline: 16 February 2015

For more information
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Summer School, June 2009 Photos from the London Pain Consortium Summer School, held 21st-26th June 2009 in El Escorial, Madrid, Spain.

There are currently no summer schools scheduled. Please continue to check the website for updates.
 
Current Research
Bioinformatics protocols exploiting text mining and function prediction algorithms to reveal pain mediating gene candidates
The aim of this project is to reveal novel insights into the molecular machinery underlying neuropathic pain. This will be achieved by means of combining existing knowledge of pain processing at the molecular level with other useful forms of biological data to make predictions of putative pain genes and networks.

For this analysis to be possible, information on genes experimentally implicated in pain processing will be extracted from the literature. This will be done by text-mining relevant published articles and querying various public functional data repositories such as the Pain Genes Database and the Mouse Genome Informatics Database. To successfully integrate this data with existing gene expression data from animal models of pain from the London Pain Database, and to deal with the multi-factorial nature of pain, each gene entry in the database will be annotated with a set of controlled vocabulary terms expressing the associated pain phenotype, the underlying experimental condition causing this phenotype and the anatomical location in which the level of expression of the gene has been showed to be altered in a correlated manner with the observed pain phenotype. Thus, an essential part of this project will be to extend the London Pain Database to include domain specific experimentally derived gene expression data as well as incorporating controlled vocabulary from anatomy, phenotype and disease/pathology encoding ontologies.

In order to predict novel pain genes, this project will look to reveal previously uncharacterised associations with the pain genes from the London Pain Database. This will be done with the use of FuncNet: a suite of function predicting tools utilising various types of biological data such as structural, genomic-context and protein-protein interaction data, developed jointly with bioinformaticians from across Europe. Statistical frameworks for integrating these predictions and increase their confidence will be researched in collaboration with Prof John Shawe-Taylor, department of computer science, UCL.