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

Combining the interactome and deleterious SNP predictions to improve disease gene identification.

Human mutation - 1 Mar 2009

Care M A, Bradford J R, Needham C J, Bulpitt A J, Westhead D R

Abstract excerpt

A method has been developed for the prediction of proteins involved in genetic disorders. This involved combining deleterious SNP prediction with a system based on protein interactions and phenotype distances; this is the first time that deleterious SNP prediction has been used to make predictions across linkage-intervals. At each step we tested and selected the best procedure, revealing that the computationally...

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 Genetic Predisposition to Disease

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Polymorphism, Single Nucleotide

Share to Protein Binding

Share to Protein Interaction Mapping

Share to Proteins

Share to Reproducibility of Results

Source

Identifiers

PMID: 19156842
DOI: 10.1002/humu.20917

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

Combining the interactome and deleterious SNP predictions to improve disease gene identification.PMID 19156842Read ↗

Analysis of genome-wide association study data using the protein knowledge base.Semantic proximity Read ↗

Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritizationSemantic proximity Read ↗

The SNPcurator: literature mining of enriched SNP-disease associations.Semantic proximity Read ↗

PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.Semantic proximity Read ↗

Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization.Semantic proximity Read ↗

Association of genes to genetically inherited diseases using data mining.Semantic proximity Read ↗

LDGIdb: a database of gene interactions inferred from long-range strong linkage disequilibrium between pairs of SNPs.Semantic proximity Read ↗

Thoughts from SNP-SIG 2012: future challenges in the annotation of genetic variations.Semantic proximity Read ↗

Congruency in the prediction of pathogenic missense mutations: state-of-the-art web-based tools.Semantic proximity Read ↗

Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.Semantic proximity Read ↗

Recent advances in predicting gene–disease associationsSemantic proximity Read ↗

E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.Semantic proximity Read ↗

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