Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information

By Emidio Capriotti, Remo Calabrese, Rita Casadio. 2006

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In Genetics

In Molecular Biology

Citations in the corpus

Better prediction of functional effects for sequence variants (2015, neutral): “We extracted 22,858 human disease-associated variants in 3,537 proteins from OMIM [24] and HumVar [25].”
Improving the prediction of disease-related variants using protein three-dimensional structure (2011, methodology): “Machine learning-based approaches such as PhD-SNP [12] and SNAP [9] have shown better results with respect to traditional approaches.”
PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations (2014, methodology): “Therearemany computational tools for prediction of the effects of amino acid substitution on protein function, e.g., MutPred [10], nsSNPAnalyzer [11], PolyPhen-1 (PPH-1) [12], PolyPhen-2 (PPH-2) [13], SNAP [14], MAPP [15], PANTHER [16], PhD-SNP [17], SIFT [18] and SNPs&GO [19].”
Pharmspresso: a text mining tool for extraction of pharmacogenomic concepts and relationships from full text (2009, methodology): “Others have reported more complex methods – sometimes more domain-specific and usually more computationally expensive for finding gene and variant mentions [12-17].”
Functional annotations improve the predictive score of human disease‐related mutations in proteins (2009, methodology): “We also compare SNPs&GO with our previous method (HybridMeth [Capriotti et al., 2006]) based only on sequence and profile information.”
The impact of next-generation sequencing on genomics (2011, background): “Predictor of human Deleterious Single Nucleotide Polymorphisms (PhD-SNP) is an optimized program to predict if a given single point protein mutation can be classified as disease-related or as neutral polymorphism based on protein sequence and profile information (Capriotti et al., 2006).”
Development of Bioinformatics Infrastructure for Genomics Research in H3Africa (2017, positive): “…Vapor, a variant analysis workflow, which combines the predictions of Provean [Choi et al., 2012], PolyPhen-2 [Adzhubei et al., 2013], PhD-SNP [Capriotti et al., 2006], FATHMM [Shihab et al., 2013], AUTO-MUTE [Masso & Vaisman, 2010], MuPro [Cheng et al., 2006], and IMutant 2.0 to predict the…”
WS-SNPs&GO: a web server for predicting the deleterious effect of human protein variants using functional annotation (2013, methodology): “These methods are mainly based on statistical and/or machine learning approaches that take as input information from protein sequence [18,19,23,24,26,28,29], structure [20,25] and function [19,21,27].”
Disease-related mutations predicted to impact protein function (2012, methodology): “This was expected due to the important overlap between our data and the training set of PhD-SNP [29].”
Performance of mutation pathogenicity prediction methods on missense variants (2011, methodology): “…of mutations and SNPs were predicted by the programs MutPred [Li et al., 2009], nsSNPAnalyzer [Bao et 2005], Panther [Thomas et al., 2003], PhD-SNP [Capriotti et al., 2006], PolyPhen [Ramensky et al., 2002], PolyPhen2 [Adzhubei et al., 2010], SIFT [Ng and Henikoff, 2001], SNAP [Bromberg and Rost,…”
Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models (2012, methodology): “…Methods) comparing the performance of FATHMM against eight published computational prediction meth-ods [Adzhubei et al., 2010; Calabrese et al., 2009; Capriotti et al., 2006; Ferrer-Costa et al., 2004; Li et al., 2009; Mort et al., 2010; Ng and Henikoff, 2001; Ramensky et al., 2002; Thomas et al.,…”