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

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration

16 Dec 2022

Abstract excerpt

BACKGROUND: Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. METHODS: The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3)...

Read the complete abstract on PubMed

Topics

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

Share to Adipokines, Inflammation, and Metabolic Diseases
Share to Bioinformatics and Genomic Networks
Share to Genetic Associations and Epidemiology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 36527108
DOI: 10.1186/s12967-022-03826-5

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 8 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integrationPMID 36527108Read ↗

Multi-omics regulatory networks reveal the key driver genes in the development of type 2 diabetesSemantic proximity Read ↗

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

Multi-tissue multi-omics integration reveals tissue-specific pathways, gene networks and drug candidates for type 1 diabetes.Semantic proximity Read ↗

Elucidating the Type 2 Diabetes Regulatory Network: Identification of EGFR as a Key Hub and Novel Drug CandidatesSemantic proximity Read ↗

Discovering hub genes involved in the pathophysiological impact of COVID-19 on diabetes kidney disease by differential gene expression and interactome 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 ↗

Bioinformatics and network biology approach to identifying type 2 diabetes genes and pathways that influence the progression of breast cancerSemantic proximity Read ↗

Robust identification of shared key genomic biomarkers for diagnosis and therapies of pancreatic cancer with type-2 diabetesSemantic proximity Read ↗

Bioinformatics analysis to disclose shared molecular mechanisms between type-2 diabetes and clear-cell renal-cell carcinoma, and therapeutic indicationsSemantic proximity Read ↗

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

Epidermal growth factor receptor (EGFR) as a key regulator in type 2 diabetes: Drug discovery insightsSemantic proximity Read ↗

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression AnalysisSemantic proximity Read ↗

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