Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics Analysis

1 May 2020

Abstract excerpt

Background: Type 2 diabetes mellitus (T2DM) is one of the most common chronic diseases in the world with complicated pathogenesis. This study aimed to identify differentially expressed genes (DEGs) and molecular pathways in T2DM using bioinformatics analysis. Materials and Methods: To explore potential therapeutic targets for T2DM, we analyzed three microarray datasets (GSE50397, GSE38642, and GSE44035) acquired...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Bioinformatics and Genomic Networks
Share to Genetic Associations and Epidemiology
Share to Rheumatoid Arthritis Research and Therapies

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 32547141
DOI: 10.2147/dmso.s245165

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of Hub Genes in Type 2 Diabetes Mellitus Using Bioinformatics AnalysisPMID 32547141Read ↗

Analysis of key genes and pathways associated with the pathogenesis of Type 2 diabetes mellitusSemantic proximity Read ↗

Differently Expressed Genes (DEGs) Relevant to Type 2 Diabetes Mellitus Identification and Pathway Analysis via Integrated Bioinformatics AnalysisSemantic proximity Read ↗

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysisSemantic proximity Read ↗

Identification of candidate biomarkers and pathways associated with type 1 diabetes mellitus using bioinformatics analysisSemantic proximity Read ↗

Identification and analysis of key genes and signaling pathways associated with Type 1 diabetes mellitus by bioinformatics analysis of next generation sequencing dataSemantic proximity Read ↗

<i>In-silico</i> analysis reveals hub genes and enriched pathways in Type 2 Diabetes MellitusSemantic proximity Read ↗

Gene expression profiling and protein-protein network analysis revealed prognostic hub biomarkers linking cancer risk in type 2 diabetic patientsSemantic proximity Read ↗

Investigation of the pathogenesis of liver cirrhosis associated with type 2 diabetes mellitus via bioinformatic analysisSemantic proximity Read ↗

Investigation of hub genes involved in diabetic nephropathy using biological informatics methodsSemantic proximity Read ↗

Bioinformatics Analysis of Next Generation Sequencing Data Identifies Molecular Biomarkers Associated With Type 2 Diabetes MellitusSemantic proximity Read ↗

Highly perturbed genes and hub genes associated with type 2 diabetes in different tissues of adult humans: A bioinformatics analytic workflowSemantic proximity Read ↗

Bioinformatics Analysis of next generation sequencing data for Risk Prediction in Patients with Type 1 diabetes mellitusSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.