Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving…mehr
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein interaction sites, and identifying protein drug targets. The book emphasizes novel approaches to protein-ligand interactions, including machine learning and deep learning, presenting a state-of-the-art suite of skills for researchers. The volume represents a resource for scientists, detailing the fundamentals of computational methods, showing how to use computational algorithms to study protein interaction data, and giving scientific explanations for biological data through computational intelligence. Fourteen chapters offer a comprehensive guide to protein interaction data and computational intelligence methods for protein-ligand interactions.
Bing Wang is Dean in the School of Electrical & Electronics Information at Anhui University of Technology, in China. He received his PhD in bioinformatics from the University of Technology, China, on protein-protein interaction site prediction. He has held postdoctoral positions in machine learning, chemoinformatics, and biomedical information engineering at the Universities of Louisville and Vanderbilt, in the USA. He has published over 120 papers, is a senior member of IEEE, and serves on the editorial board of several journals.
Inhaltsangabe
1. Computational intelligence methods in protein-ligand interactions 2. Random forest method for predicting protein ligand-binding residues 3. Encoders of protein residues for identifying protein-protein interacting residues 4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods 5. Semi-supervised prediction of protein interaction sites from unlabeled sample information 6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm 7. Evolutional algorithms and their applications in protein long-range contact prediction 8. A novel robust geometric approach for modelling protein-protein interaction networks 9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis 10. Ensemble learning-based prediction on drug-target interactions 11. Convolutional neural networks for drug-target interaction prediction 12. Ensemble learning methods for drug-induced liver injury identification 13. Database construction for mutant protein interactions 14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy
1. Computational intelligence methods in protein-ligand interactions 2. Random forest method for predicting protein ligand-binding residues 3. Encoders of protein residues for identifying protein-protein interacting residues 4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods 5. Semi-supervised prediction of protein interaction sites from unlabeled sample information 6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm 7. Evolutional algorithms and their applications in protein long-range contact prediction 8. A novel robust geometric approach for modelling protein-protein interaction networks 9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis 10. Ensemble learning-based prediction on drug-target interactions 11. Convolutional neural networks for drug-target interaction prediction 12. Ensemble learning methods for drug-induced liver injury identification 13. Database construction for mutant protein interactions 14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy
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