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

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.

International journal of biological sciences - 1 Jan 2014

Yang Lei, Zhao Xudong, Tang Xianglong

Abstract excerpt

Network biology integrates different kinds of data, including physical or functional networks and disease gene sets, to interpret human disease. A clique (maximal complete subgraph) in a protein-protein interaction network is a topological module and possesses inherently biological significance. A disease-related clique possibly associates with complex diseases. Fully identifying disease components in a clique is...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology
Share to Data Mining

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Models, Biological

Share to Protein Interaction Mapping

Share to Protein Interaction Maps

Share to Proteins

Share to Signal Transduction

Source

Open in PubMed Open full text

Identifiers

PMID: 25013377
DOI: 10.7150/ijbs.8430
PMCID: PMC4081603

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Predicting disease-related proteins based on clique backbone in protein-protein interaction network.PMID 25013377Read ↗

Prediction of human disease-related gene clusters by clustering analysis.Semantic proximity Read ↗

Prioritization of potential candidate disease genes by topological similarity of protein-protein interaction network and phenotype data.Semantic proximity Read ↗

Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.Semantic proximity Read ↗

Decipher the connections between proteins and phenotypes.Semantic proximity Read ↗

Prioritization of orphan disease-causing genes using topological feature and GO similarity between proteins in interaction networks.Semantic proximity Read ↗

Disease associated protein-protein interaction network reconstruction based on comprehensive influence analysisSemantic proximity Read ↗

The expanded human disease network combining protein-protein interaction information.Semantic proximity Read ↗

A survey on computational approaches to identifying disease biomarkers based on molecular networks.Semantic proximity Read ↗

Inferring gene-phenotype associations via global protein complex network propagation.Semantic proximity Read ↗

Network-based prediction and knowledge mining of disease genes.Semantic proximity Read ↗

C3: Connect separate Connected Components to form a succinct disease moduleSemantic proximity Read ↗

Discovering disease-genes by topological features in human protein-protein interaction network.Semantic proximity Read ↗

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