The following pages link to PseAAC (Q28339):
Displaying 31 items.
- Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (Q304850) (← 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)
- Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (Q739232) (← 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)
- Predicting protein fold pattern with functional domain and sequential evolution information (Q1617370) (← links)
- SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic \(K\)-nearest neighbor (ET-KNN) algorithm (Q1628874) (← links)
- Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition (Q1642606) (← links)
- Some remarks on protein attribute prediction and pseudo amino acid composition (Q1670702) (← links)
- BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC (Q1712641) (← 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)
- 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)
- Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach (Q1717326) (← links)
- Global and local prediction of protein folding rates based on sequence autocorrelation information (Q1719912) (← links)
- Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine (Q1783532) (← links)
- Adaptive compressive learning for prediction of protein-protein interactions from primary sequence (Q1783649) (← 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)
- Predicting protein structural class based on multi-features fusion (Q1795105) (← links)
- Enzymes/non-enzymes classification model complexity based on composition, sequence, 3D and topological indices (Q1797512) (← links)
- Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image (Q1797606) (← links)
- UPFPSR: a ubiquitylation predictor for plant through combining sequence information and random forest (Q2130126) (← links)
- Prediction of protein submitochondria locations based on data fusion of various features of sequences (Q2261649) (← links)
- Prediction of \(\beta\)-lactamase and its class by Chou's pseudo-amino acid composition and support vector machine (Q2351316) (← links)
- NL MIND-BEST: a web server for ligands and proteins discovery -- theoretic-experimental study of proteins of \textit{Giardia lamblia} and new compounds active against \textit{Plasmodium falciparum} (Q2413852) (← 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)
