A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes

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Kringel , D , Lippmann , C , Parnham , M J , Kalso , E , Ultsch , A & Lötsch , J 2018 , ' A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes ' , European Journal of Pain , vol. 22 , no. 10 , pp. 1735-1756 . https://doi.org/10.1002/ejp.1270

Title: A machine-learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes
Author: Kringel, D.; Lippmann, C.; Parnham, M. J.; Kalso, E.; Ultsch, A.; Lötsch, J.
Contributor: University of Helsinki, Eija Kalso / Principal Investigator
Date: 2018-11
Language: eng
Number of pages: 22
Belongs to series: European Journal of Pain
ISSN: 1090-3801
URI: http://hdl.handle.net/10138/256029
Abstract: Background Human genetic research has implicated functional variants of more than one hundred genes in the modulation of persisting pain. Artificial intelligence and machine-learning techniques may combine this knowledge with results of genetic research gathered in any context, which permits the identification of the key biological processes involved in chronic sensitization to pain. MethodsResultsBased on published evidence, a set of 110 genes carrying variants reported to be associated with modulation of the clinical phenotype of persisting pain in eight different clinical settings was submitted to unsupervised machine-learning aimed at functional clustering. Subsequently, a mathematically supported subset of genes, comprising those most consistently involved in persisting pain, was analysed by means of computational functional genomics in the Gene Ontology knowledgebase. Clustering of genes with evidence for a modulation of persisting pain elucidated a functionally heterogeneous set. The situation cleared when the focus was narrowed to a genetic modulation consistently observed throughout several clinical settings. On this basis, two groups of biological processes, the immune system and nitric oxide signalling, emerged as major players in sensitization to persisting pain, which is biologically highly plausible and in agreement with other lines of pain research. ConclusionsSignificanceThe present computational functional genomics-based approach provided a computational systems-biology perspective on chronic sensitization to pain. Human genetic control of persisting pain points to the immune system as a source of potential future targets for drugs directed against persisting pain. Contemporary machine-learned methods provide innovative approaches to knowledge discovery from previous evidence. We show that knowledge discovery in genetic databases and contemporary machine-learned techniques can identify relevant biological processes involved in Persitent pain.
Subject: INTERLEUKIN-1 RECEPTOR ANTAGONIST
IRRITABLE-BOWEL-SYNDROME
VULVAR VESTIBULITIS SYNDROME
LUMBAR DISC DISEASE
POSTHERPETIC NEURALGIA PHN
CYCLOHYDROLASE 1 HAPLOTYPE
DATA SCIENCE APPROACH
LOW-BACK-PAIN
NITRIC-OXIDE
KNEE OSTEOARTHRITIS
3126 Surgery, anesthesiology, intensive care, radiology
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