ArticleBMC bioinformatics2006
Predicting deleterious nsSNPs: an analysis of sequence and structural attributes.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Missense and Intronic Variants inInternational journal of medical sciences · 2026Article
- Modeling the Effects of Single Nucleotide Polymorphisms (SNPs) on the Structure and Function of the HumanHuman mutation · 2026Article
- Delving Into the Depths ofHuman mutation · 2026Article
- Article
- Rapid discrimination between deleterious and benign missense mutations in the CAGI 6 experiment.Human genomics · 2024Article
- Prediction of the most deleterious non-synonymous SNPs in the human IL1B gene: evidence from bioinformatics analyses.BMC genomic data · 2024Article
- Pathogenic single nucleotide polymorphisms in RhoA gene: Insights into structural and functional impacts on RhoA-PLD1 interaction through molecular dynamics simulation.Current research in structural biology · 2024Article
- DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2'-O-Dimethyladenosine Sites in RNA Sequences.International journal of molecular sciences · 2022Article
- Article
- Pathogenic nsSNPs that increase the risks of cancers among the Orang Asli and Malays.Scientific reports · 2021Article
- Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives.Human genetics · 2019Review
- Factor XIII polymorphism and risk of aneurysmal subarachnoid haemorrhage in a south Indian population.BMC medical genetics · 2018Article
- Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene.Scientific reports · 2017Article
- Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.PloS one · 2017Article
- GESPA: classifying nsSNPs to predict disease association.BMC bioinformatics · 2015Article
- CoagVDb: a comprehensive database for coagulation factors and their associated SAPs.Biological research · 2015Article
- Prediction and prioritization of rare oncogenic mutations in the cancer Kinome using novel features and multiple classifiers.PLoS computational biology · 2014Article
- Application of Hybrid Functional Groups to Predict ATP Binding Proteins.ISRN computational biology · 2014Article
- Article
- The role of balanced training and testing data sets for binary classifiers in bioinformatics.PloS one · 2013Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundThere has been an explosion in the number of single nucleotide polymorphisms (SNPs) within public databases. In this study we focused on non-synonymous protein coding single nucleotide polymorphisms (nsSNPs), some associated with disease and others which are thought to be neutral. We describe the distribution of both types of nsSNPs using structural and sequence based features and assess the relative value of these attributes as predictors of function using machine learning methods. We also address the common problem of balance within machine learning methods and show the effect of imbalance on nsSNP function prediction. We show that nsSNP function prediction can be significantly improved by 100% undersampling of the majority class. The learnt rules were then applied to make predictions of function on all nsSNPs within Ensembl.
resultsThe measure of prediction success is greatly affected by the level of imbalance in the training dataset. We found the balanced dataset that included all attributes produced the best prediction. The performance as measured by the Matthews correlation coefficient (MCC) varied between 0.49 and 0.25 depending on the imbalance. As previously observed, the degree of sequence conservation at the nsSNP position is the single most useful attribute. In addition to conservation, structural predictions made using a balanced dataset can be of value.
conclusionThe predictions for all nsSNPs within Ensembl, based on a balanced dataset using all attributes, are available as a DAS annotation. Instructions for adding the track to Ensembl are at http://www.brightstudy.ac.uk/das_help.html.
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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.