RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest
type: Item
instance of: scholarly article
title: RFCRYS: sequence-based protein crystallization propensity prediction by means of random forest
zbMATH Open document ID: 1397.92530
author: Samad Jahandideh
author: Abbas Mahdavi
published in: Journal of Theoretical Biology
publication date: 2018-09-27T00:00:00Z
Mathematics Subject Classification ID: 92D20
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6944776
zbMATH Keywords: random forest algorithm
zbMATH Keywords: X-ray crystallography
zbMATH Keywords: protein structure
Wikidata QID: Q51351700
describes a project that uses: ParCrys
describes a project that uses: CRYSTALP2
describes a project that uses: iDNA-Prot
describes a project that uses: NR-2L
describes a project that uses: iLoc-Euk
describes a project that uses: iLoc-Plant
describes a project that uses: Cell-PLoc
describes a project that uses: randomForest
describes a project that uses: iLoc-Gpos
describes a project that uses: iLoc-Virus
describes a project that uses: AFP-Pred
describes a project that uses: RSARF
MaRDI profile type: Publication
full work available at URL: j.jtbi.2012.04.028
OpenAlex ID: W1970247646
cites work: Random forests
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cites work: \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites
cites work: Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
cites work: Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
DOI: 10.1016/J.JTBI.2012.04.028
description: scientific article; zbMATH DE number 6944776