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Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes
Högskolan i Skövde, Forskningscentrum för Systembiologi. (Bioinformatics, Bioinformatik)
Högskolan i Skövde, Institutionen för biovetenskap. Högskolan i Skövde, Forskningscentrum för Systembiologi. (Bioinformatics, Bioinformatik)
2016 (engelsk)Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, nr 6, artikkel-id e0156006Artikkel i tidsskrift (Fagfellevurdert) Published
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Abstract [en]

Type 1 diabetes (T1D) is a complex disease, caused by the autoimmune destruction of the insulin producing pancreatic beta cells, resulting in the body?s inability to produce insulin. While great efforts have been put into understanding the genetic and environmental factors that contribute to the etiology of the disease, the exact molecular mechanisms are still largely unknown. T1D is a heterogeneous disease, and previous research in this field is mainly focused on the analysis of single genes, or using traditional gene expression profiling, which generally does not reveal the functional context of a gene associated with a complex disorder. However, network-based analysis does take into account the interactions between the diabetes specific genes or proteins and contributes to new knowledge about disease modules, which in turn can be used for identification of potential new biomarkers for T1D. In this study, we analyzed public microarray data of T1D patients and healthy controls by applying a systems biology approach that combines network-based Weighted Gene Co-Expression Network Analysis (WGCNA) with functional enrichment analysis. Novel co-expression gene network modules associated with T1D were elucidated, which in turn provided a basis for the identification of potential pathways and biomarker genes that may be involved in development of T1D.

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Public Library of Science , 2016. Vol. 11, nr 6, artikkel-id e0156006
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Bioinformatik
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URN: urn:nbn:se:his:diva-12398DOI: 10.1371/journal.pone.0156006ISI: 000377369700028PubMedID: 27257970Scopus ID: 2-s2.0-84973455067OAI: oai:DiVA.org:his-12398DiVA, id: diva2:935154
Tilgjengelig fra: 2016-06-10 Laget: 2016-06-10 Sist oppdatert: 2019-11-18

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