Computational Intelligence in Protein-Ligand Interaction Analysis

Häftad, Engelska, 2024

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Beskrivning

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

Produktinformation

Utgivningsdatum:2024-03-26
Mått:152 x 229 x 16 mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Antal sidor:278
Förlag:Elsevier Science
ISBN:9780128243862

Utforska kategorier

Biovetenskap inom Naturvetenskap och teknik
Biologi inom Naturvetenskap och teknik

Mer om författaren

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. Peng Chen is a Professor in the Institute of Physical Science and Information Technology, and the School of the Internet, at Anhui University, China. He received his PhD from the University of Science and Technology of China (USTC). He has held international roles in Hong Kong, the USA, and Saudi Arabia. He was previously Associate Professor at the Hefei Institute of Intelligent Machines, with the Chinese Academy of Sciences. His research interests include machine learning, and data mining, and he has published over 100 papers. Jun Zhang is a Professor at Anhui University, China, and is an expert in the field of computational intelligence.

Innehållsförteckning

1. Computational intelligence methods in protein-ligand interactions2. Random forest method for predicting protein ligand-binding residues3. Encoders of protein residues for identifying protein-protein interacting residues4. Identification of hot spot residues in protein interfaces from protein sequences and ensemble methods5. Semi-supervised prediction of protein interaction sites from unlabeled sample information6. Developing computational model to predict protein-protein interaction sites based on XGBoost algorithm7. Evolutional algorithms and their applications in protein long-range contact prediction8. A novel robust geometric approach for modelling protein-protein interaction networks9. Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis10. Ensemble learning-based prediction on drug-target interactions11. Convolutional neural networks for drug-target interaction prediction12. Ensemble learning methods for drug-induced liver injury identification13. Database construction for mutant protein interactions14. A linear programming computational framework integrates phosphor-proteomics and prior knowledge to predict drug efficacy