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A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes
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 id...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220816/ https://www.ncbi.nlm.nih.gov/pubmed/29923268 http://dx.doi.org/10.1002/ejp.1270 |
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author | Kringel, D. Lippmann, C. Parnham, M.J. Kalso, E. Ultsch, A. Lötsch, J. |
author_facet | Kringel, D. Lippmann, C. Parnham, M.J. Kalso, E. Ultsch, A. Lötsch, J. |
author_sort | Kringel, D. |
collection | PubMed |
description | 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. METHODS: Based 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. RESULTS: 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. CONCLUSIONS: The 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. SIGNIFICANCE: We show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain. |
format | Online Article Text |
id | pubmed-6220816 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-62208162018-11-13 A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes Kringel, D. Lippmann, C. Parnham, M.J. Kalso, E. Ultsch, A. Lötsch, J. Eur J Pain Original Article 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. METHODS: Based 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. RESULTS: 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. CONCLUSIONS: The 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. SIGNIFICANCE: We show that knowledge discovery in genetic databases and contemporary machine‐learned techniques can identify relevant biological processes involved in Persitent pain. John Wiley and Sons Inc. 2018-07-13 2018-11 /pmc/articles/PMC6220816/ /pubmed/29923268 http://dx.doi.org/10.1002/ejp.1270 Text en © 2018 The Authors. European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation EFIC® This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Kringel, D. Lippmann, C. Parnham, M.J. Kalso, E. Ultsch, A. Lötsch, J. A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title | A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title_full | A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title_fullStr | A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title_full_unstemmed | A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title_short | A machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
title_sort | machine‐learned analysis of human gene polymorphisms modulating persisting pain points to major roles of neuroimmune processes |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6220816/ https://www.ncbi.nlm.nih.gov/pubmed/29923268 http://dx.doi.org/10.1002/ejp.1270 |
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