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

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

PLoS computational biology - 1 May 2014

Zhao Nan, Han Jing Ginger, Shyu Chi-Ren, Korkin Dmitry

Abstract excerpt

Single nucleotide polymorphisms (SNPs) are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs) have been found near or inside the protein-protein interaction (PPI) interfaces. Determining whether...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Artificial Intelligence
Share to Breast Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Diabetes Mellitus

Share to Genetic Association Studies

Share to Pattern Recognition, Automated

Share to Protein Interaction Mapping

Share to Proteome

Source

Open in PubMed Open full text

Identifiers

PMID: 24784581
DOI: 10.1371/journal.pcbi.1003592
PMCID: PMC4006705

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

Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.PMID 24784581Read ↗

Predicting interaction-specific protein–protein interaction perturbations by missense variants with MutPred-PPISemantic proximity Read ↗

Approaches and resources for prediction of the effects of non-synonymous single nucleotide polymorphism on protein function and interactions.Semantic proximity Read ↗

In-Silico Methods for Investigating the Effect of Single Nucleotide Polymorphisms on the Structure and Function of Proteins: A ReviewSemantic proximity Read ↗

SAAMBE-3D: Predicting Effect of Mutations on Protein-Protein Interactions.Semantic proximity Read ↗

LS-SNP: large-scale annotation of coding non-synonymous SNPs based on multiple information sources.Semantic proximity Read ↗

PinSnps: structural and functional analysis of SNPs in the context of protein interaction networks.Semantic proximity Read ↗

Functional annotations improve the predictive score of human disease-related mutations in proteins.Semantic proximity Read ↗

Predicting functional consequences of mutations using molecular interaction network features.Semantic proximity Read ↗

Predicting functional consequences of mutations using molecular interaction network featuresSemantic proximity Read ↗

Computational identification of non-synonymous polymorphisms within regions corresponding to protein interaction sites.Semantic proximity Read ↗

Combination use of protein-protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphisms.Semantic proximity Read ↗

In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review.Semantic proximity Read ↗

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