CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition (Q647294)

scientific article; zbMATH DE number 5977488

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English	CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition	scientific article; zbMATH DE number 5977488

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instance of

scholarly article

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title

CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition (English)

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Computational Biology and Chemistry

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publication date

22 November 2011

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zbMATH Keywords

dipeptide composition

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describes a project that uses

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full work available at URL

https://doi.org/10.1016/j.compbiolchem.2011.05.003

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cites work

Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates

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Some remarks on protein attribute prediction and pseudo amino acid composition

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An ensemble of models of the acute inflammatory response to bacterial lipopolysaccharide in rats: results from parameter space reduction

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SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic \(K\)-nearest neighbor (ET-KNN) algorithm

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Pattern classification.

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Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition

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A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM

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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy

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Predicting \(O\)-glycosylation sites in mammalian proteins by using SVMs

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The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition

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Combination of support vector machines using genetic programming

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A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction

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Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins

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Lysine acetylation sites prediction using an ensemble of support vector machine classifiers

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A novel representation for apoptosis protein subcellular localization prediction using support vector machine

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Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern

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Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence

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Identifiers

zbMATH Open document ID

1226.92020

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Mathematics Subject Classification ID

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journals/candc/KhanMH11

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DOI

10.1016/J.COMPBIOLCHEM.2011.05.003

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Sitelinks

Mathematics(1 entry)

mardi Publication:647294