Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image

DOI10.1016/j.jtbi.2008.06.016zbMath1400.92416OpenAlexW1980827173WikidataQ34797295 ScholiaQ34797295MaRDI QIDQ1797606

Kuo-Chen K.-C. Chou, Xuan Xiao, Pu. Wang

Publication date: 22 October 2018

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2008.06.016

zbMATH Keywords

cellular automatonimage texturepseudo amino acid compositionChou's invariant theoremcovariant-discriminant algorithmgeometric invariant momentspace-time evolution

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10) Protein sequences, DNA sequences (92D20) Cellular automata (computational aspects) (68Q80)

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Uses Software

• Cell-PLoc
• PseAAC

Cites Work

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• Prediction of structural classes for protein sequences and domains--impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy
• The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
• Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification
• Using pseudo-amino acid composition and support vector machine to predict protein structural class
• Prediction of the subcellular location of apoptosis proteins
• Novel scales based on hydrophobicity indices for secondary protein structure
• Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition
• Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
• Prediction of membrane protein types from sequences and position-specific scoring matrices

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