Identification of dysregulated modules based on network entropy in type 1 diabetes

Type 1 diabetes is a prevalent autoimmune disease of which the underlying mechanisms remain to be elucidated. The aim of the study was to identify dysregulated modules of type 1 diabetes. After microarray data were preprocessed, 20,545 genes were obtained. By integrating gene expression data and pro...

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Detalles Bibliográficos
Autores principales: Zheng, Yan, Liu, Liwei, Ye, Jifeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2018
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5841047/
https://www.ncbi.nlm.nih.gov/pubmed/29545837
http://dx.doi.org/10.3892/etm.2018.5803
Descripción
Sumario:Type 1 diabetes is a prevalent autoimmune disease of which the underlying mechanisms remain to be elucidated. The aim of the study was to identify dysregulated modules of type 1 diabetes. After microarray data were preprocessed, 20,545 genes were obtained. By integrating gene expression data and protein-protein interactions (PPI) data, 48,778 new networks were obtained, including 7,953 genes. After simplifying networks, we obtained 24 target networks. By ranking networks with P-values, two modules with P<0.05 were identified, including the genes, CCNB1, CDC45, GINS2, NDC80, FBXO5, NCAPG and DLGAP5. Module 2 was part of module 1. The identified modules and genes may provide new insights into the underlying biological mechanisms that drive the progression of type 1 diabetes.

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