The following pages link to propy (Q34194):
Displaying 26 items.
- Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (Q304850) (← links)
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (Q305612) (← links)
- Naïve Bayes classifier with feature selection to identify phage virion proteins (Q382613) (← 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)
- Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition (Q739676) (← links)
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition (Q739723) (← links)
- iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou's general PseAAC (Q739749) (← 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)
- pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (Q1712835) (← links)
- iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (Q1716822) (← links)
- pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (Q1717058) (← 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)
- SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins (Q1734238) (← links)
- Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components (Q1739305) (← links)
- Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions (Q1790746) (← 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)
- Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine (Q2351316) (← links)
- Discrimination of acidic and alkaline enzyme using Chou's pseudo amino acid composition in conjunction with probabilistic neural network model (Q2351334) (← links)
- Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (Q2415541) (← links)
- Chou's pseudo amino acid composition improves sequence-based antifreeze protein prediction (Q2415547) (← 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)
- iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints (Q2632182) (← links)
- Predicting anticancer peptides with Chou's pseudo amino acid composition and investigating their mutagenicity via ames test (Q2632389) (← links)
