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

Blind prediction of deleterious amino acid variations with SNPs&GO.

Human mutation - 1 Sept 2017

Capriotti Emidio, Martelli Pier Luigi, Fariselli Piero, Casadio Rita

Abstract excerpt

SNPs&GO is a machine learning method for predicting the association of single amino acid variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a support vector machine algorithm to discriminate between disease-related and neutral SAVs. SNPs&GO combines information from protein sequence with functional annotation encoded by gene ontology (GO)...

Read the complete abstract on PubMed

Topics

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

Share to Acid Anhydride Hydrolases
Share to Checkpoint Kinase 2
Share to Cyclin-Dependent Kinase Inhibitor p16

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to DNA Repair Enzymes

Share to Gene Ontology

Share to Molecular Sequence Annotation

Share to ROC Curve

Share to Support Vector Machine

Source

Open in PubMed Open full text

Identifiers

PMID: 28102005
DOI: 10.1002/humu.23179
PMCID: PMC5522651

Genes

CHEK2
NAGLU
RAD50

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Blind prediction of deleterious amino acid variations with SNPs&GO.PMID 28102005Read ↗

WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation.Semantic proximity Read ↗

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

Evaluation of computational techniques for predicting non-synonymous single nucleotide variants pathogenicity.Semantic proximity Read ↗

A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.Semantic proximity Read ↗

PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants.Semantic proximity Read ↗

Sequence-based prediction of pathological mutations.Semantic proximity Read ↗

Comparative analysis of in-silico tools in identifying pathogenic variants in dominant inherited retinal diseases.Semantic proximity Read ↗

A benchmark study of scoring methods for non-coding mutations.Semantic proximity Read ↗

SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequenceSemantic proximity Read ↗

Assessment of 13 in silico pathogenicity methods on cancer-related variants.Semantic proximity Read ↗

The SAAP pipeline and database: tools to analyze the impact and predict the pathogenicity of mutations.Semantic proximity Read ↗

LYRUS: A Machine Learning Model for Predicting the Pathogenicity of Missense VariantsSemantic proximity Read ↗

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