A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition

DOI10.1016/j.jtbi.2012.12.008zbMath1406.92471OpenAlexW2125463451WikidataQ30424872 ScholiaQ30424872MaRDI QIDQ1715164

Abdollah Dehzangi, Alok Sharma, Kuldip K. Paliwal, J. E. Lyons

Publication date: 4 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.2012.12.008

zbMATH Keywords

protein sequenceprotein fold recognitionbi-gram featuresposition specific scoring matrix (PSSM)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)

Related Items (8)

Improving the prediction accuracy of protein structural class: approached with alternating word frequency and normalized Lempel-Ziv complexity ⋮ Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces ⋮ Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou's general PseAAC ⋮ Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping ⋮ PSSM-Suc: accurately predicting succinylation using position specific scoring matrix into bigram for feature extraction ⋮ iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features ⋮ Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition ⋮ A two-layer classification framework for protein fold recognition

Uses Software

• LIBSVM
• Cell-PLoc
• PSI-BLAST
• BLAST
• AFP-Pred
• MemHyb
• PRMLT

Cites Work

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• A method to distinguish between lysine acetylation and lysine methylation from protein sequences
• Predicting protein fold pattern with functional domain and sequential evolution information
• A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
• A protein fold classifier formed by fusing different modes of pseudo amino acid composition via PSSM
• Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition
• Some remarks on protein attribute prediction and pseudo amino acid composition
• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
• Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern
• MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM
• Prediction of protein-protein interaction sites using patch-based residue characterization
• Improvements to Platt's SMO Algorithm for SVM Classifier Design

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