Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices

DOI10.1016/j.jtbi.2008.06.003zbMath1400.92405OpenAlexW1976093800WikidataQ33349646 ScholiaQ33349646MaRDI QIDQ1797512

Cristian Robert Munteanu, Alexandre L. Magalhães, Humberto González Díaz

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.2008.06.003

zbMATH Keywords

star graphprotein modelsprotein secondary structuresPython application

Mathematics Subject Classification ID

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

Related Items (8)

Alignment-free prediction of mycobacterial DNA promoters based on pseudo-folding lattice network or star-graph topological indices ⋮ Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation ⋮ Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates ⋮ The structural complexity of DNA templates -- implications on cellular complexity ⋮ A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer ⋮ Markov mean properties for cell death-related protein classification ⋮ Some remarks on protein attribute prediction and pseudo amino acid composition ⋮ Unravelling the relationship between protein sequence and low-complexity regions entropies: interactome implications

Uses Software

• STATISTICA
• Cell-PLoc
• PseAAC
• EzyPred

Cites Work

• Predicting enzyme family classes by hybridizing gene product composition and pseudo-amino acid composition
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