Using genetic algorithms to select most predictive protein features.
about
Predicting Bevirimat resistance of HIV-1 from genotypeInsights into the classification of small GTPasesImproved Bevirimat resistance prediction by combination of structural and sequence-based classifiersMachine learning on normalized protein sequences.Automatic quantitative MRI texture analysis in small-for-gestational-age fetuses discriminates abnormal neonatal neurobehavior.Effective automated feature construction and selection for classification of biological sequences.Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).A computational approach for the identification of small GTPases based on preprocessed amino acid sequences.A homology/ab initio hybrid algorithm for sampling near-native protein conformations.
P2860
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P2860
Using genetic algorithms to select most predictive protein features.
description
2009 nî lūn-bûn
@nan
2009 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2009 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
name
Using genetic algorithms to select most predictive protein features.
@ast
Using genetic algorithms to select most predictive protein features.
@en
Using genetic algorithms to select most predictive protein features.
@nl
type
label
Using genetic algorithms to select most predictive protein features.
@ast
Using genetic algorithms to select most predictive protein features.
@en
Using genetic algorithms to select most predictive protein features.
@nl
prefLabel
Using genetic algorithms to select most predictive protein features.
@ast
Using genetic algorithms to select most predictive protein features.
@en
Using genetic algorithms to select most predictive protein features.
@nl
P2860
P356
P1433
P1476
Using genetic algorithms to select most predictive protein features.
@en
P2093
Andrew Kernytsky
P2860
P356
10.1002/PROT.22211
P407
P577
2009-04-01T00:00:00Z