Prediction of protein structure class by coupling improved genetic algorithm and support vector machine.
about
PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotationsA strategy to select suitable physicochemical attributes of amino acids for protein fold recognition.Proposing a highly accurate protein structural class predictor using segmentation-based features.Customised fragments libraries for protein structure prediction based on structural class annotations.Sequence physical properties encode the global organization of protein structure space.iFC²: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content.Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures.Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm.Prediction of Protein Structural Class Based on Gapped-Dipeptides and a Recursive Feature Selection Approach.Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM).Accurate prediction of the burial status of transmembrane residues of α-helix membrane protein by incorporating the structural and physicochemical features.Structural classification of proteins using texture descriptors extracted from the cellular automata image.Selection of relevant features from amino acids enables development of robust classifiers.Learning protein multi-view features in complex space.Identifying subcellular localizations of mammalian protein complexes based on graph theory with a random forest algorithm.A new model of flavonoids affinity towards P-glycoprotein: genetic algorithm-support vector machine with features selected by a modified particle swarm optimization algorithm.Accurate prediction of protein structural class using auto covariance transformation of PSI-BLAST profiles.
P2860
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P2860
Prediction of protein structure class by coupling improved genetic algorithm and support vector machine.
description
2008 nî lūn-bûn
@nan
2008 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Prediction of protein structur ...... hm and support vector machine.
@ast
Prediction of protein structur ...... hm and support vector machine.
@en
type
label
Prediction of protein structur ...... hm and support vector machine.
@ast
Prediction of protein structur ...... hm and support vector machine.
@en
prefLabel
Prediction of protein structur ...... hm and support vector machine.
@ast
Prediction of protein structur ...... hm and support vector machine.
@en
P2093
P2860
P1433
P1476
Prediction of protein structur ...... hm and support vector machine.
@en
P2093
P2860
P2888
P304
P356
10.1007/S00726-008-0084-Z
P577
2008-04-22T00:00:00Z