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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...

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Detalles Bibliográficos
Autores principales: Pihur, Vasyl, Datta, Somnath, Datta, Susmita
Formato: Texto
Lenguaje:English
Publicado: Biomedical Informatics 2011
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.
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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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