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swMATH24441MaRDI QIDQ36199

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Related Items (31)

RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression ⋮ NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC ⋮ Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition ⋮ Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC ⋮ The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textit{in-silico} analyses ⋮ 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 ⋮ Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's 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 ⋮ Large-scale frequent stem pattern mining in RNA families ⋮ iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences ⋮ Predicting membrane protein types by incorporating a novel feature set into Chou's general PseAAC ⋮ 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 ⋮ Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou's general PseAAC ⋮ Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC ⋮ \textit{In silico} analysis of \textit{plasmodium falciparum} CDPK5 protein through molecular modeling, docking and dynamics ⋮ pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments ⋮ MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components ⋮ Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions ⋮ 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 ⋮ Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins ⋮ Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC ⋮ DeepMRMP: a new predictor for multiple types of RNA modification sites using deep learning

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