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

Uncover disease genes by maximizing information flow in the phenome-interactome network.

Bioinformatics (Oxford, England) - 1 Jul 2011

Chen Yong, Jiang Tao, Jiang Rui

Abstract excerpt

MOTIVATION: Pinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein-protein interactions, no combinatorial approaches have been proposed in the literature....

Read the complete abstract on PubMed

Topics

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

Share to Gene Dosage
Share to Genetic Predisposition to Disease
Share to Melanoma

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Phenotype

Share to Proteins

Source

Open in PubMed Open full text

Identifiers

PMID: 21685067
DOI: 10.1093/bioinformatics/btr213
PMCID: PMC3117332

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

Uncover disease genes by maximizing information flow in the phenome-interactome network.PMID 21685067Read ↗

Prioritizing protein complexes implicated in human diseases by network optimization.Semantic proximity Read ↗

PITCH: A Pathway-Induced Prioritization of Personalized Cancer Driver Genes Based on Higher-Order Interactions.Semantic proximity Read ↗

Prioritising risk pathways of complex human diseases based on functional profiling.Semantic proximity Read ↗

Walking on multiple disease-gene networks to prioritize candidate genes.Semantic proximity Read ↗

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

PSEA: A phenotypic similarity ensemble approach for prioritizes candidate genes to aid mendelian disease diagnosisSemantic proximity Read ↗

Pinpointing disease genes through phenomic and genomic data fusion.Semantic proximity Read ↗

Towards prediction and prioritization of disease genes by the modularity of human phenome-genome assembled network.Semantic proximity Read ↗

PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer DriversSemantic proximity Read ↗

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

Prioritizing disease genes with an improved dual label propagation framework.Semantic proximity Read ↗

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

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