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

A graph centrality-based approach for candidate gene prediction for type 1 diabetes.

Immunologic research - 1 Oct 2021

Thummadi N B, Vishnu E, Subbiah E V, Manimaran P

Abstract excerpt

Type 1 diabetes mellitus (T1DM) or insulin-dependent diabetes is an autoimmune disease that may pose life-threatening situations to individuals. In most cases, cytotoxic T lymphocytes (CTLs) promotes killing of islets of Langerhans in the pancreas, which harbour insulin-producing beta cells. The trigger for autoimmune attack is still unclear; therefore, identifying and targeting candidate genes are imperative to...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Biomarkers
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Graphics

Share to Databases, Genetic

Share to Diabetes Mellitus, Type 1

Share to Gene Expression Regulation

Share to User-Computer Interface

Source

Identifiers

PMID: 34297307
DOI: 10.1007/s12026-021-09217-0

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A graph centrality-based approach for candidate gene prediction for type 1 diabetes.PMID 34297307Read ↗

Identification of new key genes for type 1 diabetes through construction and analysis of protein–protein interaction networks based on blood and pancreatic islet transcriptomesSemantic proximity Read ↗

In silico analysis reveals interrelation of enriched pathways and genes in type 1 diabetes.Semantic proximity Read ↗

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

Identification of Key Genes and Pathways in Peripheral Blood Mononuclear Cells of Type 1 Diabetes Mellitus by Integrated Bioinformatics AnalysisSemantic proximity Read ↗

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

Discovering the diagnostic biomarkers underlying Type 1 diabetes and Celiac disease by integrating transcriptomics and machine learningSemantic proximity Read ↗

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

Measuring the Importance of Vertices in the Weighted Human Disease NetworkSemantic proximity Read ↗

A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association StudiesSemantic proximity Read ↗

Candidate genes expressed in human islets and their role in the pathogenesis of type 1 diabetes.Semantic proximity Read ↗

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 DiabetesSemantic proximity Read ↗

Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis.Semantic proximity Read ↗

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