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Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data
We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of tw...
Autores principales: | , , |
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Formato: | Texto |
Lenguaje: | English |
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Biomedical Informatics
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089886/ https://www.ncbi.nlm.nih.gov/pubmed/21584188 |
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author | Pihur, Vasyl Datta, Somnath Datta, Susmita |
author_facet | Pihur, Vasyl Datta, Somnath Datta, Susmita |
author_sort | Pihur, Vasyl |
collection | PubMed |
description | We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway. |
format | Text |
id | pubmed-3089886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-30898862011-05-16 Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data Pihur, Vasyl Datta, Somnath Datta, Susmita Bioinformation Prediction Model We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway. Biomedical Informatics 2011-04-22 /pmc/articles/PMC3089886/ /pubmed/21584188 Text en © 2011 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited. |
spellingShingle | Prediction Model Pihur, Vasyl Datta, Somnath Datta, Susmita Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title | Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title_full | Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title_fullStr | Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title_full_unstemmed | Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title_short | Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data |
title_sort | meta analysis of chronic fatigue syndrome through integration of clinical, gene expression, snp and proteomic data |
topic | Prediction Model |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3089886/ https://www.ncbi.nlm.nih.gov/pubmed/21584188 |
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