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swMATH22447MaRDI QIDQ34239

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Related Items (24)

Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties ⋮ RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression ⋮ Prediction of posttranslational modification sites from amino acid sequences with kernel methods ⋮ Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping ⋮ Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology ⋮ Human proteins characterization with subcellular localizations ⋮ An effective haplotype assembly algorithm based on hypergraph partitioning ⋮ Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm ⋮ BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC ⋮ pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC ⋮ Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence ⋮ Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition ⋮ iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC ⋮ Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach ⋮ iRNA-PseKNC(2methyl): identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components ⋮ Identifying N\(^6\)-methyladenosine sites using extreme gradient boosting system optimized by particle swarm optimizer ⋮ SPrenylC-PseAAC: a sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins ⋮ Dforml(KNN)-PseAAC: detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components ⋮ Prediction of interface residue based on the features of residue interaction network ⋮ Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC ⋮ Prediction of protein structure classes by incorporating different protein descriptors into general Chou's pseudo amino acid composition ⋮ 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

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