Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms

DOI10.1016/j.jtbi.2010.07.026zbMath1407.92082OpenAlexW1990537176WikidataQ30391767 ScholiaQ30391767MaRDI QIDQ1726107

Iosif I. Vaisman, Majid Masso

Publication date: 19 February 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2010.07.026

zbMATH Keywords

computational geometrymachine learningprotein structurestatistical potentialvariant

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20) Genetics and epigenetics (92D10)

Related Items (7)

Predicting DNA binding proteins using support vector machine with hybrid fractal features ⋮ Using protein granularity to extract the protein sequence features ⋮ Sequence-dependent prediction of recombination hotspots in \textit{Saccharomyces cerevisiae} ⋮ A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences ⋮ Two-intermediate model to characterize the structure of fast-folding proteins ⋮ \textit{In vitro} transcriptomic prediction of hepatotoxicity for early drug discovery ⋮ AUTO-MUTE

Uses Software

• Qhull
• Cell-PLoc
• UniProt
• GPCR-GIA
• Quat-2L
• SNPs3D
• nsSNPAnalyzer
• PMut

Cites Work

• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
• The quickhull algorithm for convex hulls
• Random forests

This page was built for publication: Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms