The following pages link to ESLpred (Q34195):
Displaying 13 items.
- Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (Q738768) (← links)
- Sequence-driven features for prediction of subcellular localization of proteins (Q850129) (← links)
- Max-min distance nonnegative matrix factorization (Q889348) (← links)
- A novel representation for apoptosis protein subcellular localization prediction using support vector machine (Q1624357) (← links)
- SubChlo: predicting protein subchloroplast locations with pseudo-amino acid composition and the evidence-theoretic \(K\)-nearest neighbor (ET-KNN) algorithm (Q1628874) (← links)
- Geometry preserving projections algorithm for predicting membrane protein types (Q1628998) (← links)
- Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou's pseudo-amino acid composition (Q1642606) (← links)
- Prediction of protein submitochondria locations based on data fusion of various features of sequences (Q2261649) (← links)
- Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms (Q2373315) (← links)
- Feature extraction by statistical contact potentials and wavelet transform for predicting subcellular localizations in gram negative bacterial proteins (Q2413900) (← links)
- Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (Q2415583) (← links)
- Using nearest feature line and tunable nearest neighbor methods for prediction of protein subcellular locations (Q2490503) (← links)
- Predicting Gram-positive bacterial protein subcellular localization based on localization motifs (Q2632058) (← links)
