The following pages link to iPPI-Esml (Q34205):
Displaying 27 items.
- Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (Q304850) (← links)
- pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (Q305612) (← links)
- Discriminate protein decoys from native by using a scoring function based on ubiquitous phi and psi angles computed for all atom (Q738564) (← links)
- Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (Q738670) (← links)
- Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (Q738768) (← links)
- Classification of membrane protein types using voting feature interval in combination with Chou's pseudo amino acid composition (Q739723) (← links)
- 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 (Q739749) (← 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)
- Characterization of BioPlex network by topological properties (Q1664450) (← links)
- Prediction of metastasis in advanced colorectal carcinomas using CGH data (Q1704333) (← links)
- BlaPred: predicting and classifying \(\beta\)-lactamase using a 3-tier prediction system via Chou's general PseAAC (Q1712641) (← 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)
- iMethyl-STTNC: identification of N\(^6\)-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences (Q1714298) (← links)
- Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition (Q1714359) (← links)
- Effective DNA binding protein prediction by using key features via Chou's general PseAAC (Q1716796) (← links)
- iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (Q1716822) (← links)
- Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou's general PseAAC (Q1716873) (← links)
- MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components (Q1717066) (← links)
- Predicting protein-protein interactions by fusing various Chou's pseudo components and using wavelet denoising approach (Q1717326) (← 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)
- iEnhancer-MFGBDT: Identifying enhancers and their strength by fusing multiple features and gradient boosting decision tree (Q2092240) (← links)
- Massive datasets and machine learning for computational biomedicine: trends and challenges (Q2329887) (← links)
