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swMATH22422MaRDI QIDQ34214

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Related Items (19)

NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC ⋮ Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC ⋮ Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach ⋮ Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach ⋮ Characterization of BioPlex network by topological properties ⋮ Prediction of metastasis in advanced colorectal carcinomas using CGH data ⋮ 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 ⋮ Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou's general PseAAC via Kullback-Leibler divergence ⋮ 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 ⋮ 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 ⋮ 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 ⋮ Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection ⋮ iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree

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