Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern
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Publication:1788146
DOI10.1016/j.jtbi.2007.09.014zbMath1397.92551OpenAlexW1967888993WikidataQ50878953 ScholiaQ50878953MaRDI QIDQ1788146
Kuo-Chen Chou, Yong-Sheng Ding, Tong-Liang Zhang
Publication date: 8 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.2007.09.014
approximate entropypseudo-amino acid compositionfuzzy KNN classifierhydrophobicity patternprotein structure classes
Applications of statistics to biology and medical sciences; meta analysis (62P10) Pattern recognition, speech recognition (68T10) Protein sequences, DNA sequences (92D20)
Related Items (24)
Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design ⋮ Use of fuzzy clustering technique and matrices to classify amino acids and its impact to Chou's pseudo amino acid composition ⋮ Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices ⋮ Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation ⋮ Predict potential drug targets from the ion channel proteins based on SVM ⋮ A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction ⋮ Novel 3D bio-macromolecular bilinear descriptors for protein science: predicting protein structural classes ⋮ Improving the prediction accuracy of protein structural class: approached with alternating word frequency and normalized Lempel-Ziv complexity ⋮ Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition ⋮ Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces ⋮ A protein structural classes prediction method based on PSI-BLAST profile ⋮ Using protein granularity to extract the protein sequence features ⋮ Some remarks on protein attribute prediction and pseudo amino acid composition ⋮ Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping ⋮ Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC ⋮ CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition ⋮ A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition ⋮ Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses ⋮ A classification-based prediction model of messenger RNA polyadenylation sites ⋮ A study of entropy/clarity of genetic sequences using metric spaces and fuzzy sets ⋮ Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition ⋮ Predicting protein structural class based on multi-features fusion ⋮ Natural/random protein classification models based on star network topological indices ⋮ LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs
Uses Software
Cites Work
- The method to compare nucleotide sequences based on the minimum entropy principle
- Prediction of structural classes for protein sequences and domains--impact of prediction algorithms, sequence representation and homology, and test procedures on accuracy
- Prediction of protein subcellular location using hydrophobic patterns of amino acid sequence
- Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition
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- Pseudo amino acid composition and multi-class support vector machines approach for conotoxin superfamily classification
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- 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
- Approximate entropy as a measure of system complexity.
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