ArticleHuman mutation2017
Blind prediction of deleterious amino acid variations with SNPs&GO.
Article in Human mutation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Computational identification of rare pathogenic genomic variants in esophageal cancer markers: Transcript-level analysis, sequence-based insights, and structural-functional impacts of non-synonymous SNPs.Biochemistry and biophysics reports · 2026Article
- In silico prioritisation of EDN1 missense variants identifies mature endothelin-1 substitutions with predicted receptor-binding effects.BMC genomics · 2026Article
- Predicting Pathogenicity ofInternational journal of molecular sciences · 2026Article
- An integrative coding and non-coding SNPs analysis of theFrontiers in oncology · 2026Article
- Association Between FABP3 and FABP4 Genes with Changes in Milk Composition and Fatty Acid Profiles in the Native Southern Yellow Cattle Breed.Veterinary sciences · 2025Article
- Assessing predictions on fitness effects of missense variants in HMBS in CAGI6.Human genetics · 2025Article
- Article
- Comprehensive computational analysis of deleterious nsSNPs inMolecular biology research communications · 2025Article
- Exploring the Structural and Functional Consequences of Deleterious Missense Nonsynonymous SNPs in theJournal of personalized medicine · 2024Article
- A Novel Missense SNP in the Fatty Acid-Binding Protein 4 (FABP4) Gene is Associated with Growth Traits in Karakul and Awassi Sheep.Biochemical genetics · 2024Article
- A computational approach to analyzing the functional and structural impacts of Tripeptidyl-Peptidase 1 missense mutations in neuronal ceroid lipofuscinosis.Metabolic brain disease · 2024Article
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods.Genome biology · 2024Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- Identification of potential therapeutic intervening targets by in-silico analysis of nsSNPs in preterm birth-related genes.PloS one · 2023Article
- In silico identification of the rare-coding pathogenic mutations and structural modeling of human NNAT gene associated with anorexia nervosa.Eating and weight disorders : EWD · 2022Article
- Evaluating the relevance of sequence conservation in the prediction of pathogenic missense variants.Human genetics · 2022Article
- Assessing the performance of in silico methods for predicting the pathogenicity of variants in the gene CHEK2, among Hispanic females with breast cancer.Human mutation · 2019Article
- Reports from CAGI: The Critical Assessment of Genome Interpretation.Human mutation · 2017Article
Corrections and comments
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4 authors.
Funding
Abstract
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) terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve of 0.88 with low false-positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs&GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper, we summarize the best results obtained by SNPs&GO on disease-related variations of four CAGI challenges relative to the following genes: CHEK2 (CAGI 2010), RAD50 (CAGI 2011), p16-INK (CAGI 2013), and NAGLU (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.