ArticlePloS one2012
FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 50 citations in OpenAlex.
- Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors.Human genomics · 2024Article
- Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors.bioRxiv : the preprint server for biology · 2024Article
- Machine learning model of the catalytic efficiency and substrate specificity of acyl-ACP thioesterase variants generated from natural andFrontiers in bioengineering and biotechnology · 2024Article
- ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.Briefings in bioinformatics · 2023Article
- C10Pred: A First Machine Learning Based Tool to Predict C10 Family Cysteine Peptidases Using Sequence-Derived Features.International journal of molecular sciences · 2022Article
- PPVED: A machine learning tool for predicting the effect of single amino acid substitution on protein function in plants.Plant biotechnology journal · 2022Article
- ASPIRER: a new computational approach for identifying non-classical secreted proteins based on deep learning.Briefings in bioinformatics · 2022Article
- Prediction of disease-associated nsSNPs by integrating multi-scale ResNet models with deep feature fusion.Briefings in bioinformatics · 2022Article
- VIPdb, a genetic Variant Impact Predictor Database.Human mutation · 2019Article
- iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites.Briefings in bioinformatics · 2019Review
- Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.Briefings in bioinformatics · 2018Review
- DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.Oncotarget · 2018Article
- PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector Machine.Frontiers in microbiology · 2018Article
- Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.PloS one · 2017Article
- GlycoMineScientific reports · 2016Article
- Computational assessment of feature combinations for pathogenic variant prediction.Molecular genetics & genomic medicine · 2016Article
- Discriminating between deleterious and neutral non-frameshifting indels based on protein interaction networks and hybrid properties.Molecular genetics and genomics : MGG · 2015Article
- Analysis of genetic variation and potential applications in genome-scale metabolic modeling.Frontiers in bioengineering and biotechnology · 2015Review
- Principal component analysis of binding energies for single-point mutants of hT2R16 bound to an agonist correlate with experimental mutant cell response.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single amino acid variants (SAVs) are the most abundant form of known genetic variations associated with human disease. Successful prediction of the functional impact of SAVs from sequences can thus lead to an improved understanding of the underlying mechanisms of why a SAV may be associated with certain disease. In this work, we constructed a high-quality structural dataset that contained 679 high-quality protein structures with 2,048 SAVs by collecting the human genetic variant data from multiple resources and dividing them into two categories, i.e., disease-associated and neutral variants. We built a two-stage random forest (RF) model, termed as FunSAV, to predict the functional effect of SAVs by combining sequence, structure and residue-contact network features with other additional features that were not explored in previous studies. Importantly, a two-step feature selection procedure was proposed to select the most important and informative features that contribute to the prediction of disease association of SAVs. In cross-validation experiments on the benchmark dataset, FunSAV achieved a good prediction performance with the area under the curve (AUC) of 0.882, which is competitive with and in some cases better than other existing tools including SIFT, SNAP, Polyphen2, PANTHER, nsSNPAnalyzer and PhD-SNP. The sourcecodes of FunSAV and the datasets can be downloaded at http://sunflower.kuicr.kyoto-u.ac.jp/sjn/FunSAV.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.