Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition
From MaRDI portal
Publication:1670554
DOI10.1016/j.jtbi.2010.11.017zbMath1405.92217OpenAlexW2024767324WikidataQ51630484 ScholiaQ51630484MaRDI QIDQ1670554
Publication date: 6 September 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2010.11.017
Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20) Cell biology (92C37)
Related Items
Protein subcellular localization in human and hamster cell lines: employing local ternary patterns of fluorescence microscopy images ⋮ Predicting DNA binding proteins using support vector machine with hybrid fractal features ⋮ A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC ⋮ Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces ⋮ Using protein granularity to extract the protein sequence features ⋮ 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 ⋮ Detrended cross-correlation coefficient: application to predict apoptosis protein subcellular localization ⋮ Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology ⋮ A set of descriptors for identifying the protein-drug interaction in cellular networking ⋮ iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences ⋮ Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC ⋮ CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition ⋮ Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition ⋮ A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition ⋮ MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components ⋮ iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components ⋮ MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM ⋮ Sequence-dependent prediction of recombination hotspots in \textit{Saccharomyces cerevisiae} ⋮ Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition ⋮ \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites ⋮ A novel canonical dual computational approach for prion AGAAAAGA amyloid fibril molecular modeling ⋮ Studies on the rules of \(\beta\)-strand alignment in a protein \(\beta\)-sheet structure ⋮ Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition ⋮ Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space ⋮ Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition
Uses Software
Cites Work
- Machine learning based adaptive watermark decoding in view of anticipated attack
- A novel representation for apoptosis protein subcellular localization prediction using support vector machine
- Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
- A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer
- The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
- Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition
- Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
- Unnamed Item
- Unnamed Item