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

Discovering cancer genes by integrating network and functional properties.

BMC medical genomics - 19 Sept 2009

Li Li, Zhang Kangyu, Lee James, Cordes Shaun, Davis David P, Tang Zhijun

Abstract excerpt

BACKGROUND: Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO)...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Cell Survival
Share to Cluster Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Databases, Genetic

Share to Gene Regulatory Networks

Share to Protein Interaction Mapping

Share to Proteins

Share to RNA Interference

Source

Open in PubMed Open full text

Identifiers

PMID: 19765316
DOI: 10.1186/1755-8794-2-61
PMCID: PMC2758898

Genes

DLD

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

Discovering cancer genes by integrating network and functional properties.PMID 19765316Read ↗

Connectivity mapping using a combined gene signature from multiple colorectal cancer datasets identified candidate drugs including existing chemotherapiesSemantic proximity Read ↗

Prediction of oncogenic interactions and cancer-related signaling networks based on network topology.Semantic proximity Read ↗

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures.Semantic proximity Read ↗

Identifying dysregulated pathways in cancers from pathway interaction networksSemantic proximity Read ↗

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

Systems Biology Approach to Gene Network Analysis Identifies Therapeutic and Diagnostic Targets in Colorectal CancerSemantic proximity Read ↗

A Boolean-based systems biology approach to predict novel genes associated with cancer: Application to colorectal cancer.Semantic proximity Read ↗

Integration of genetic variants and gene network for drug repurposing in colorectal cancer.Semantic proximity Read ↗

A network-based drug repositioning infrastructure for precision cancer medicine through targeting significantly mutated genes in the human cancer genomes.Semantic proximity Read ↗

CAERUS: predicting CAncER oUtcomeS using relationship between protein structural information, protein networks, gene expression data, and mutation data.Semantic proximity Read ↗

Enhancing Cancer Driver Gene Prediction by Protein-Protein Interaction Network.Semantic proximity Read ↗

Integration strategy is a key step in network-based analysis and dramatically affects network topological properties and inferring outcomes.Semantic proximity Read ↗

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