228 related articles for article (PubMed ID: 28028462)
1. Network-based analysis of differentially expressed genes in cerebrospinal fluid (CSF) and blood reveals new candidate genes for multiple sclerosis.
Safari-Alighiarloo N; Rezaei-Tavirani M; Taghizadeh M; Tabatabaei SM; Namaki S
PeerJ; 2016; 4():e2775. PubMed ID: 28028462
[TBL] [Abstract][Full Text] [Related]
2. Identification of common key genes and pathways between type 1 diabetes and multiple sclerosis using transcriptome and interactome analysis.
Safari-Alighiarloo N; Taghizadeh M; Mohammad Tabatabaei S; Namaki S; Rezaei-Tavirani M
Endocrine; 2020 Apr; 68(1):81-92. PubMed ID: 31912409
[TBL] [Abstract][Full Text] [Related]
3. Identification of new key genes for type 1 diabetes through construction and analysis of protein-protein interaction networks based on blood and pancreatic islet transcriptomes.
Safari-Alighiarloo N; Taghizadeh M; Tabatabaei SM; Shahsavari S; Namaki S; Khodakarim S; Rezaei-Tavirani M
J Diabetes; 2017 Aug; 9(8):764-777. PubMed ID: 27625010
[TBL] [Abstract][Full Text] [Related]
4. Immune and Epstein-Barr virus gene expression in cerebrospinal fluid and peripheral blood mononuclear cells from patients with relapsing-remitting multiple sclerosis.
Veroni C; Marnetto F; Granieri L; Bertolotto A; Ballerini C; Repice AM; Schirru L; Coghe G; Cocco E; Anastasiadou E; Puopolo M; Aloisi F
J Neuroinflammation; 2015 Jul; 12():132. PubMed ID: 26169064
[TBL] [Abstract][Full Text] [Related]
5. Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells.
Shang Z; Sun W; Zhang M; Xu L; Jia X; Zhang R; Fu S
PeerJ; 2020; 8():e8357. PubMed ID: 32117605
[TBL] [Abstract][Full Text] [Related]
6. Global map of physical interactions among differentially expressed genes in multiple sclerosis relapses and remissions.
Tuller T; Atar S; Ruppin E; Gurevich M; Achiron A
Hum Mol Genet; 2011 Sep; 20(18):3606-19. PubMed ID: 21676896
[TBL] [Abstract][Full Text] [Related]
7. Centrality Analysis of Protein-Protein Interaction Networks and Molecular Docking Prioritize Potential Drug-Targets in Type 1 Diabetes.
Soofi A; Taghizadeh M; Tabatabaei SM; Rezaei Tavirani M; Shakib H; Namaki S; Safari Alighiarloo N
Iran J Pharm Res; 2020; 19(4):121-134. PubMed ID: 33841528
[TBL] [Abstract][Full Text] [Related]
8. Gene expression profiling in multiple sclerosis: a disease of the central nervous system, but with relapses triggered in the periphery?
Brynedal B; Khademi M; Wallström E; Hillert J; Olsson T; Duvefelt K
Neurobiol Dis; 2010 Mar; 37(3):613-21. PubMed ID: 19944761
[TBL] [Abstract][Full Text] [Related]
9. Varicella-zoster virus in cerebrospinal fluid at relapses of multiple sclerosis.
Sotelo J; Martínez-Palomo A; Ordoñez G; Pineda B
Ann Neurol; 2008 Mar; 63(3):303-11. PubMed ID: 18306233
[TBL] [Abstract][Full Text] [Related]
10. Identification of Key Pathways and Genes in Anaplastic Thyroid Carcinoma via Integrated Bioinformatics Analysis.
Hu S; Liao Y; Chen L
Med Sci Monit; 2018 Sep; 24():6438-6448. PubMed ID: 30213925
[TBL] [Abstract][Full Text] [Related]
11. Tumor necrosis factor-alfa and interleukin-4 in cerbrospinal fluid and plasma in different clinical forms of multiple sclerosis.
Obradović D; Kataranovski M; Dincić E; Obradović S; Colić M
Vojnosanit Pregl; 2012 Feb; 69(2):151-6. PubMed ID: 22500369
[TBL] [Abstract][Full Text] [Related]
12. Network analysis of inflammatory responses to sepsis by neutrophils and peripheral blood mononuclear cells.
Godini R; Fallahi H; Ebrahimie E
PLoS One; 2018; 13(8):e0201674. PubMed ID: 30086151
[TBL] [Abstract][Full Text] [Related]
13. Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis.
Guo Y; Bao Y; Ma M; Yang W
Int J Mol Sci; 2017 Mar; 18(4):. PubMed ID: 28350360
[TBL] [Abstract][Full Text] [Related]
14. Molecular mechanisms underlying gliomas and glioblastoma pathogenesis revealed by bioinformatics analysis of microarray data.
Vastrad B; Vastrad C; Godavarthi A; Chandrashekar R
Med Oncol; 2017 Sep; 34(11):182. PubMed ID: 28952134
[TBL] [Abstract][Full Text] [Related]
15. Identification of the miRNA-mRNA regulatory network in multiple sclerosis.
Yang Q; Pan W; Qian L
Neurol Res; 2017 Feb; 39(2):142-151. PubMed ID: 27809691
[TBL] [Abstract][Full Text] [Related]
16. Network-Based Meta-Analyses of Associations of Multiple Gene Expression Profiles with Bone Mineral Density Variations in Women.
He H; Cao S; Niu T; Zhou Y; Zhang L; Zeng Y; Zhu W; Wang YP; Deng HW
PLoS One; 2016; 11(1):e0147475. PubMed ID: 26808152
[TBL] [Abstract][Full Text] [Related]
17. Construction of Parkinson's disease marker-based weighted protein-protein interaction network for prioritization of co-expressed genes.
George G; Valiya Parambath S; Lokappa SB; Varkey J
Gene; 2019 May; 697():67-77. PubMed ID: 30776463
[TBL] [Abstract][Full Text] [Related]
18. Candidate genes and microRNAs for glioma pathogenesis and prognosis based on gene expression profiles.
Xie C; Xu M; Lu D; Zhang W; Wang L; Wang H; Li J; Ren F; Wang C
Mol Med Rep; 2018 Sep; 18(3):2715-2723. PubMed ID: 30015885
[TBL] [Abstract][Full Text] [Related]
19. Identification of differentially expressed genes, associated functional terms pathways, and candidate diagnostic biomarkers in inflammatory bowel diseases by bioinformatics analysis.
Cheng C; Hua J; Tan J; Qian W; Zhang L; Hou X
Exp Ther Med; 2019 Jul; 18(1):278-288. PubMed ID: 31258663
[TBL] [Abstract][Full Text] [Related]
20. Identification of ncRNAs as potential therapeutic targets in multiple sclerosis through differential ncRNA - mRNA network analysis.
Irizar H; Muñoz-Culla M; Sáenz-Cuesta M; Osorio-Querejeta I; Sepúlveda L; Castillo-Triviño T; Prada A; Lopez de Munain A; Olascoaga J; Otaegui D
BMC Genomics; 2015 Mar; 16(1):250. PubMed ID: 25880556
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
