Computational Intelligence in Protein-Ligand Interaction Analysis
- 1st Edition - March 26, 2024
- Latest edition
- Authors: Bing Wang, Peng Chen, Jun Zhang
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 4 3 8 6 - 2
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 4 4 3 5 - 7
Computational Intelligence in Protein-Ligand Interaction Analysis presents computational techniques for predicting protein-ligand interactions, recognizing protein intera… Read more
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
Researchers working on bioinformatics and computational intelligence algorithms, biophysics, computational biology, molecular modelling, and drug design, Graduate students in bioinformatics, protein bioinformatics, proteomics, protein engineering, structural bioinformatics, and computational biology
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
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
- Edition: 1
- Latest edition
- Published: March 26, 2024
- Language: English
BW
Bing Wang
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.
Affiliations and expertise
Dean, School of Electrical and Electronics Information, Anhui University of Technology, ChinaPC
Peng Chen
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.
Affiliations and expertise
Professor, Institute of Physical Science and Information Technology, School of the Internet, Anhui University, ChinaJZ
Jun Zhang
Jun Zhang is a Professor at Anhui University, China, and is an expert in the field of computational intelligence.
Affiliations and expertise
Professor, Anhui University, ChinaRead Computational Intelligence in Protein-Ligand Interaction Analysis on ScienceDirect