Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC

DOI10.1016/J.JTBI.2018.05.033zbMath1406.92452OpenAlexW2807459510WikidataQ56976875 ScholiaQ56976875MaRDI QIDQ1714173

Ernesto Contreras-Torres

Publication date: 31 January 2019

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

Full work available at URL: https://doi.org/10.1016/j.jtbi.2018.05.033

zbMATH Keywords

nearest neighbor0D-protein descriptoramino acid-groupamino acid-typeprotein structural classestotal sum of squares

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)

Related Items (3)

iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins

Uses Software

• LogitBoost
• repDNA
• iNuc-PseKNC
• Pse-in-One
• iPPI-Esml
• PseKNC
• iRSpot-PseDNC
• iNitro-Tyr
• iRNA-AI
• pLoc-mPlant
• iProt-Sub

Cites Work

• Unnamed Item
• A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
• IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou's pseudo amino acid composition
• Some remarks on protein attribute prediction and pseudo amino acid composition
• Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern
• Predicting protein structural class based on multi-features fusion
• Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image
• Using LogitBoost classifier to predict protein structural classes
• Predicting membrane protein type by functional domain composition and pseudo-amino acid composition
• Using pseudo-amino acid composition and support vector machine to predict protein structural class
• Novel 3D bio-macromolecular bilinear descriptors for protein science: predicting protein structural classes
• Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition

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