The following pages link to iSNO-AAPair (Q34239):
Displaying 24 items.
- Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (Q304850) (← links)
- Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition (Q739676) (← 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)
- RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression (Q1635544) (← links)
- Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm (Q1712633) (← links)
- BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC (Q1712641) (← links)
- pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (Q1712835) (← links)
- Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence (Q1714132) (← links)
- Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition (Q1714359) (← links)
- iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (Q1716822) (← links)
- Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC (Q1716885) (← links)
- pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (Q1717058) (← links)
- Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach (Q1717326) (← links)
- iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components (Q1721769) (← links)
- Identifying N\(^6\)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer (Q1730106) (← 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)
- Prediction of interface residue based on the features of residue interaction network (Q1747711) (← links)
- Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC (Q1747718) (← links)
- Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (Q2415541) (← links)
- Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (Q2415583) (← links)
- Human proteins characterization with subcellular localizations (Q2415647) (← links)
- An effective haplotype assembly algorithm based on hypergraph partitioning (Q2415650) (← links)
- Prediction of posttranslational modification sites from amino acid sequences with kernel methods (Q2632579) (← links)
