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Computational Intelligence in Protein-Ligand Interaction Analysis
- 1st Edition - March 22, 2024
- 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
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Request a sales quoteComputational 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Contributors
- Chapter 1. Random forest method for predicting protein ligand–binding residues
- 1. Introduction
- 2. Methods
- 3. Results and discussion
- 4. Conclusions
- Chapter 2. Encoders of protein residues for identifying protein–protein interacting residues
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusions
- 5. Supplementary materials
- Chapter 3. Ensemble method for the Identification of hotspot residues from protein sequences
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusion
- Chapter 4. Predicting protein interaction sites from unlabeled sample information based on a semi-supervised approach
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusion
- Chapter 5. An XGBoost-based model to predict protein–protein interaction sites
- 1. Introduction
- 2. Materials and methods
- 3. Results
- 4. Discussion
- 5. Conclusions
- Chapter 6. Evolutional algorithms and their applications in protein long-range contact prediction
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusions
- Chapter 7. A two-stage peak alignment algorithm for two-dimensional gas chromatography time-of-flight mass spectrometry data
- 1. Introduction
- 2. Methods
- 3. Results and discussion
- 4. Conclusions
- Chapter 8. Predicting drug–target interactions with electrotopological state fingerprints and amphiphilic pseudo amino acid composition
- 1. Introduction
- 2. Materials and methods
- 3. Results
- 4. Discussion
- 5. Conclusions
- Chapter 9. Ensemble learning–based prediction on drug–target interactions
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusions
- Chapter 10. Convolutional neural networks for drug–target interaction prediction
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Discussion
- 5. Case study
- 6. Conclusions
- 7. Supplementary materials
- Chapter 11. Ensemble learning methods for drug-induced liver injury identification
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusions
- 5. Supplementary materials
- Chapter 12. Database construction for mutant protein interactions
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Conclusions
- 5. Supplementary materials
- Chapter 13. Predicting drug efficacy using a linear programming computational framework
- 1. Introduction
- 2. Methods
- 3. Results
- 4. Discussion
- 5. Conclusions
- Index
- No. of pages: 310
- Language: English
- Edition: 1
- Published: March 22, 2024
- Imprint: Academic Press
- Paperback ISBN: 9780128243862
- eBook ISBN: 9780128244357
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