High performance set of PseAAC and sequence based descriptors for protein classification

DOI10.1016/j.jtbi.2010.06.006zbMath1407.92103OpenAlexW2090557416WikidataQ51688578 ScholiaQ51688578MaRDI QIDQ1722782

Sheryl Brahnam, Alessandra Lumini, Loris Nanni

Publication date: 18 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.2010.06.006

zbMATH Keywords

machine learningensemble of classifiers, support vector machinesproteins classification

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Protein sequences, DNA sequences (92D20)

Related Items (4)

A set of descriptors for identifying the protein-drug interaction in cellular networking ⋮ Adaptive compressive learning for prediction of protein-protein interactions from primary sequence ⋮ Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition ⋮ Median arc center corrected binary pattern (MACCBP) for noise robust feature extraction

Uses Software

• Cell-PLoc
• Memtype-2L
• ProtIdent
• Virus-ploc
• GPCR-GIA
• Gneg-mPLoc
• GPCR-CA
• Signal-CF
• AAindex
• Gpos-PLoc
• VirulentPred

Cites Work

• Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
• Protein functional class prediction using global encoding of amino acid sequence
• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
• Gneg-mPLoc: a top-down strategy to enhance the quality of predicting subcellular localization of Gram-negative bacterial proteins
• The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
• Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image
• Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
• Comparison among feature extraction methods for HIV-1 protease cleavage site prediction
• Dominant Local Binary Patterns for Texture Classification

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