Results 51 to 60 of about 194,990 (329)

Hub gene target of glioblastoma: LOX, SERPINH1 and TGFBI

open access: yesMedicine, 2022
Glioblastoma (GBM) is a malignant tumor. The long-term prognosis of the patients is poor. Therefore, it is of important clinical value to further explore the pathogenesis and look for molecular markers for early diagnosis and targeted treatment.
Shuyuan Zhang   +7 more
openaire   +2 more sources

Identifying hub genes of calcific aortic valve disease and revealing the immune infiltration landscape based on multiple WGCNA and single-cell sequence analysis

open access: yesFrontiers in Immunology, 2022
BackgroundCalcific aortic valve disease (CAVD) is a progressive fibrocalcific disease that can be treated only through valve replacement. This study aimed to determine the role of hub genes and immune cell infiltration in CAVD progression.MethodsIn this ...
Kan Wang   +5 more
doaj   +1 more source

Prediction and analysis of osteoarthritis hub genes with bioinformatics

open access: yesAnnals of Translational Medicine, 2023
Osteoarthritis (OA) is the most common type of arthritis. OA can cause joint pain, stiffness, and loss of function. The pathogenesis of OA is not completely clear. Moreover, there is no effective treatment, and clinical management is limited to symptomatic relief or joint surgery.
Zhong, Junqing, Xiang, Ding, Ma, Xinlong
openaire   +2 more sources

Peripheral blood gene expression reveals an inflammatory transcriptomic signature in Friedreich's ataxia patients. [PDF]

open access: yes, 2018
Transcriptional changes in Friedreich's ataxia (FRDA), a rare and debilitating recessive Mendelian neurodegenerative disorder, have been studied in affected but inaccessible tissues-such as dorsal root ganglia, sensory neurons and cerebellum-in animal ...
Coppola, Giovanni   +11 more
core   +1 more source

Network Analyses of Differentially Expressed Genes in Osteoarthritis to Identify Hub Genes [PDF]

open access: yesBioMed Research International, 2019
Background. Osteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis. Methods.
Zhaoyan Li   +8 more
openaire   +2 more sources

Identification and validation of hub genes for diabetic retinopathy

open access: yesPeerJ, 2021
Background Diabetic retinopathy (DR) is characterized by a gradually progressive alteration in the retinal microvasculature that leads to middle-aged adult acquired persistent blindness. Limited research has been conducted on DR pathogenesis at the gene level.
Li Peng, Wei Ma, Qing Xie, Baihua Chen
openaire   +3 more sources

Transcriptional landscape of epithelial and immune cell populations revealed through FACS-seq of healthy human skin. [PDF]

open access: yes, 2017
Human skin consists of multiple cell types, including epithelial, immune, and stromal cells. Transcriptomic analyses have previously been performed from bulk skin samples or from epithelial and immune cells expanded in cell culture.
Ahn, Richard S   +9 more
core   +1 more source

The tapeworm interactome: inferring confidence scored protein-protein interactions from the proteome of Hymenolepis microstoma [PDF]

open access: yes, 2020
BACKGROUND: Reference genome and transcriptome assemblies of helminths have reached a level of completion whereby secondary analyses that rely on accurate gene estimation or syntenic relationships can be now conducted with a high level of confidence ...
James, Katherine, Olson, Peter D.
core   +2 more sources

Identification of hub genes related to prognosis in glioma [PDF]

open access: yesBioscience Reports, 2020
Abstract Glioma, a common malignant tumor of the central nervous system, has high invasiveness. The objective of the present study was to identify genes playing an important role in the development of glioma and to reveal their potential research value.
Delong Zhang   +5 more
openaire   +2 more sources

Weighted-Lasso for Structured Network Inference from Time Course Data [PDF]

open access: yes, 2009
We present a weighted-Lasso method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own a prior internal
Ambroise, Christophe   +2 more
core   +2 more sources

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