Computational Intelligence in Protein-Ligand Interaction Analysis

1. Computational intelligence methods in protein-ligand interactions<br>2. Random forest method for predicting protein ligand-binding residues<br>3. Encoders of protein residues for identifying protein-protein interacting residues<br>4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods<br>5. Semi-supervised prediction of protein interaction sites from unlabeled sample information<br>6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm<br>7. Evolutional algorithms and their applications in protein long-range contact prediction<br>8. A novel robust geometric approach for modelling protein-protein interaction networks<br>9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis<br>10. Ensemble learning-based prediction on drug-target interactions<br>11. Convolutional neural networks for drug-target interaction prediction<br>12. Ensemble learning methods for drug-induced liver injury identification<br>13. Database construction for mutant protein interactions<br>14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy