iPhos-PseEn
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Related Items (16)
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 ⋮ Characterization of BioPlex network by topological properties ⋮ Personalized glucose-insulin model based on signal analysis ⋮ Prediction of S-sulfenylation sites using mRMR feature selection and fuzzy support vector machine algorithm ⋮ pLoc\_bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC ⋮ iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences ⋮ Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition ⋮ Effective DNA binding protein prediction by using key features via Chou's general PseAAC ⋮ 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 ⋮ 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 ⋮ 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 aptamer-protein interacting pairs based on sparse autoencoder feature extraction and an ensemble classifier
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