Bing Wang, Peng Chen, Jun Zhang

Computational Intelligence in Protein-Ligand Interaction Analysis (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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.

• Presents a guide to computational techniques for protein-ligand interaction analysis
• Guides researchers in developing advanced computational intelligence methods for the protein-ligand problem
• Identifies appropriate computational tools for various problems
• Demonstrates the use of advanced techniques such as vector machine, neural networks, and machine learning
• Offers the computational, mathematical and statistical skills researchers need

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 310
• Erscheinungstermin: 22. März 2024
• Englisch
• ISBN-13: 9780128244357
• Artikelnr.: 70228107

Autorenporträt

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