The following pages link to SecretP (Q34262):
Displaying 14 items.
- Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection (Q293789) (← links)
- Some remarks on protein attribute prediction and pseudo amino acid composition (Q1670702) (← links)
- Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models: key sequence positions and their amino acid properties (Q1715094) (← links)
- Protein space: a natural method for realizing the nature of protein universe (Q1715121) (← links)
- SecretP: identifying bacterial secreted proteins by fusing new features into Chou's pseudo-amino acid composition (Q1732912) (← links)
- Prediction of GABA\(_{\mathrm A}\) receptor proteins using the concept of Chou's pseudo-amino acid composition and support vector machine (Q1783532) (← links)
- Predicting mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition (Q1784371) (← links)
- A segmented principal component analysis -- regression approach to QSAR study of peptides (Q1784758) (← links)
- \textbf{iLoc-Virus}: a multi-label learning classifier for identifying the subcellular localization of virus proteins with both single and multiple sites (Q1786031) (← links)
- Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou's pseudo amino acid compositions (Q1790746) (← links)
- MemHyb: predicting membrane protein types by hybridizing SAAC and PSSM (Q2263483) (← links)
- A new hybrid fractal algorithm for predicting thermophilic nucleotide sequences (Q2263499) (← links)
- Multi-kernel transfer learning based on Chou's PseAAC formulation for protein submitochondria localization (Q2263504) (← links)
- Prediction of protein-protein interaction sites using patch-based residue characterization (Q2263508) (← links)
