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.
1. Computational Intelligence in Protein-Ligand Interaction Analysis - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Chapter 1 Random forest method for predicting protein ligand–binding residues - p. 1 8. Chapter 2 Encoders of protein residues for identifying protein–protein interacting residues - p. 27 9. Chapter 3 Ensemble method for the Identification of hotspot residues from protein sequences - p. 53 10. Chapter 4 Predicting protein interaction sites from unlabeled sample information based on a semi-supervised approach - p. 73 11. Chapter 5 An XGBoost-based model to predict protein–protein interaction sites - p. 91 12. Chapter 6 Evolutional algorithms and their applications in protein long-range contact prediction - p. 109 13. Chapter 7 A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry data - p. 131 14. Chapter 8 Predicting drug–target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition - p. 143 15. Chapter 9 Ensemble learning–based prediction on drug–target interactions - p. 163 16. Chapter 10 Convolutional neural networks for drug–target interaction prediction - p. 183 17. Chapter 11 Ensemble learning methods for drug-induced liver injury identification - p. 203 18. Chapter 12 Database construction for mutant protein interactions - p. 221 19. Chapter 13 Predicting drug efficacy using a linear programming computational framework - p. 233 20. Index - p. 255
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.
1. Computational Intelligence in Protein-Ligand Interaction Analysis - p. iii 2. Cover - p. iii 3. Title Page - p. iii 4. Copyright Page - p. iv 5. Table of Contents - p. iv 6. Contributors - p. xiii 7. Chapter 1 Random forest method for predicting protein ligand–binding residues - p. 1 8. Chapter 2 Encoders of protein residues for identifying protein–protein interacting residues - p. 27 9. Chapter 3 Ensemble method for the Identification of hotspot residues from protein sequences - p. 53 10. Chapter 4 Predicting protein interaction sites from unlabeled sample information based on a semi-supervised approach - p. 73 11. Chapter 5 An XGBoost-based model to predict protein–protein interaction sites - p. 91 12. Chapter 6 Evolutional algorithms and their applications in protein long-range contact prediction - p. 109 13. Chapter 7 A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry data - p. 131 14. Chapter 8 Predicting drug–target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition - p. 143 15. Chapter 9 Ensemble learning–based prediction on drug–target interactions - p. 163 16. Chapter 10 Convolutional neural networks for drug–target interaction prediction - p. 183 17. Chapter 11 Ensemble learning methods for drug-induced liver injury identification - p. 203 18. Chapter 12 Database construction for mutant protein interactions - p. 221 19. Chapter 13 Predicting drug efficacy using a linear programming computational framework - p. 233 20. Index - p. 255
