Statistical geometry based prediction of nonsynonymous SNP functional effects using random forest and neuro-fuzzy classifiers

Proteins
Maxim BarenboimD Curtis Jamison

Abstract

There is substantial interest in methods designed to predict the effect of nonsynonymous single nucleotide polymorphisms (nsSNPs) on protein function, given their potential relationship to heritable diseases. Current state-of-the-art supervised machine learning algorithms, such as random forest (RF), train models that classify single amino acid mutations in proteins as either neutral or deleterious to function. However, it is frequently the case that the functional effect of a polymorphism on a protein resides between these two extremes. The utilization of classifiers that incorporate fuzzy logic provides a natural extension in order to account for the spectrum of possible functional consequences. We generated a dataset of single amino acid substitutions in human proteins having known three-dimensional structures. Each variant was uniquely represented as a feature vector that included computational geometry and knowledge-based statistical potential predictors obtained though application of Delaunay tessellation of protein structures. Additional attributes consisted of physicochemical properties of the native and replacement amino acids as well as topological location of the mutated residue position in the solved structure. Clas...Continue Reading

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Citations

Aug 20, 2009·Protein Engineering, Design & Selection : PEDS·Majid MassoIosif I Vaisman
Apr 21, 2009·Pharmacogenomics·Grace S Y PangCaroline G L Lee
Sep 2, 2009·Computers in Biology and Medicine·Hassan U KalsumZalmiyah Zakaria
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Mar 23, 2011·Journal of Computational Chemistry·Shuyan LiBiao Li
Jul 25, 2015·BMC Bioinformatics·Jay K KhuranaJuilee Thakar
Jan 18, 2018·Molecular Systems Biology·Magali RichardGaël Yvert
Feb 6, 2017·Clinical & Translational Oncology : Official Publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico·I SullivanA Barnadas

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