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

Detecting disease associated modules and prioritizing active genes based on high throughput data.

BMC bioinformatics - 13 Jan 2010

Qiu Yu-Qing, Zhang Shihua, Zhang Xiang-Sun, Chen Luonan

Abstract excerpt

BACKGROUND: The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve bi...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Databases, Genetic
Share to Gene Expression Profiling

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Gene Regulatory Networks

Share to Neoplasms

Share to Oligonucleotide Array Sequence Analysis

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 20070902
DOI: 10.1186/1471-2105-11-26
PMCID: PMC2825224

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026. Prepared record publication-20070902-2026-09-02.2. Manifest publication-graph-cohort-2026-09-02.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Detecting disease associated modules and prioritizing active genes based on high throughput data.PMID 20070902Read ↗

Mining susceptibility gene modules and disease risk genes from SNP data by combining network topological properties with support vector regression.Semantic proximity Read ↗

An integrated approach to identify causal network modules of complex diseases with application to colorectal cancer.Semantic proximity Read ↗

On the limits of active module identification.Semantic proximity Read ↗

A novel candidate disease genes prioritization method based on module partition and rank fusion.Semantic proximity Read ↗

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

Identification of breast cancer risk modules via an integrated strategy.Semantic proximity Read ↗

GenePioneer: A Comprehensive Python Package for Identification of Essential Genes and Modules in CancerSemantic proximity Read ↗

Exploring phenotype-associated modules in an oral cavity tumor using an integrated framework.Semantic proximity Read ↗

Towards precise classification of cancers based on robust gene functional expression profiles.Semantic proximity Read ↗

A computational method based on the integration of heterogeneous networks for predicting disease-gene associations.Semantic proximity Read ↗

Artificial intelligence based computational framework for drug-target prioritization and inference of novel repositionable drugs for Alzheimer’s diseaseSemantic proximity Read ↗

Prioritization of candidate disease genes by topological similarity between disease and protein diffusion profiles.Semantic proximity Read ↗

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