Pages that link to "Item:Q1670554"

The following pages link to Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition (Q1670554):

Displaying 40 items.

• Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (Q304850) (← links)
• Detrended cross-correlation coefficient: application to predict apoptosis protein subcellular localization (Q343065) (← links)
• CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition (Q647294) (← links)
• Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (Q738670) (← links)
• Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (Q738768) (← links)
• Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition (Q739723) (← links)
• SLLE for predicting membrane protein types (Q776432) (← links)
• Geometry preserving projections algorithm for predicting membrane protein types (Q1628998) (← links)
• 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 (Q1649407) (← links)
• iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences (Q1714298) (← links)
• Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC (Q1714327) (← links)
• 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 (Q1715087) (← links)
• A feature extraction technique using bi-gram probabilities of position specific scoring matrix for protein fold recognition (Q1715164) (← links)
• Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses (Q1715312) (← links)
• MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components (Q1717066) (← links)
• iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components (Q1721769) (← links)
• Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine (Q1783532) (← links)
• Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition (Q1784371) (← links)
• \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites (Q1786031) (← links)
• A novel canonical dual computational approach for prion AGAAAAGA amyloid fibril molecular modeling (Q1786044) (← links)
• Studies on the rules of \(\beta\)-strand alignment in a protein \(\beta\)-sheet structure (Q1786068) (← links)
• Prediction of \(\beta\)-turn types in protein by using composite vector (Q1786366) (← links)
• Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition (Q1790807) (← links)
• The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition (Q1794478) (← links)
• Predicting membrane protein type by functional domain composition and pseudo-amino acid composition (Q2194927) (← links)
• Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition (Q2201981) (← links)
• Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition (Q2202064) (← links)
• Prediction of membrane protein types from sequences and position-specific scoring matrices (Q2219704) (← links)
• Predicting ion channels and their types by the dipeptide mode of pseudo amino acid composition (Q2261622) (← links)
• MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (Q2263483) (← links)
• Sequence-dependent prediction of recombination hotspots in \textit{Saccharomyces cerevisiae} (Q2263495) (← links)
• Application of density similarities to predict membrane protein types based on pseudo-amino acid composition (Q2413825) (← links)
• Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (Q2415583) (← links)
• A set of descriptors for identifying the protein-drug interaction in cellular networking (Q2415703) (← links)
• Application of residue distribution along the sequence for discriminating outer membrane proteins (Q2500284) (← links)
• Protein subcellular localization in human and hamster cell lines: employing local ternary patterns of fluorescence microscopy images (Q2632347) (← links)
• Predicting DNA binding proteins using support vector machine with hybrid fractal features (Q2632482) (← links)
• 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 (Q2632571) (← links)
• Prediction of protein structure classes using hybrid space of multi-profile Bayes and bi-gram probability feature spaces (Q2632581) (← links)
• Using protein granularity to extract the protein sequence features (Q2635021) (← links)