The following pages link to iRNA-AI (Q36195):
Displaying 26 items.
- RBSURFpred: modeling protein accessible surface area in real and binary space using regularized and optimized regression (Q1635544) (← links)
- NucPosPred: predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC (Q1642583) (← links)
- Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition (Q1642606) (← links)
- Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou's PseKNC (Q1642634) (← links)
- 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 (Q1649407) (← links)
- Prediction of metastasis in advanced colorectal carcinomas using CGH data (Q1704333) (← links)
- Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou's PseAAC (Q1712667) (← 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)
- Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC (Q1714173) (← links)
- Large-scale frequent stem pattern mining in RNA families (Q1714283) (← 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)
- \textit{In silico} analysis of \textit{plasmodium falciparum} CDPK5 protein through molecular modeling, docking and dynamics (Q1716916) (← links)
- pSSbond-PseAAC: prediction of disulfide bonding sites by integration of PseAAC and statistical moments (Q1717058) (← links)
- MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components (Q1717066) (← links)
- Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions (Q1717294) (← 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)
- Bi-PSSM: position specific scoring matrix based intelligent computational model for identification of mycobacterial membrane proteins (Q1749056) (← links)
- Prediction of protein subcellular localization with oversampling approach and Chou's general PseAAC (Q1752399) (← links)
- Sequence-based discrimination of protein-RNA interacting residues using a probabilistic approach (Q2400938) (← links)
- The preliminary efficacy evaluation of the CTLA-4-ig treatment against lupus nephritis through \textit{in-silico} analyses (Q2415808) (← links)
