Artificial Intelligence in Bioinformatics and Chemoinformatics
Gebonden Engels 2023 1e druk 9781032396576Samenvatting
The authors aim to shed light on the practicality of using machine learning in finding complex chemoinformatics and bioinformatics applications as well as identifiying AI in biological and chemical data points. The chapters are designed in such a way that they highlight the important role of AI in chemistry and bioinformatics particularly for the classification of diseases, selection of features and compounds, dimensionality reduction and more. In addition, they assist in the organization and optimal use of data points generated from experiments performed using AI techniques. This volume discusses the development of automated tools and techniques to aid in research plans.
Features
Covers AI applications in bioinformatics and chemoinformatics
Demystifies the involvement of AI in generating biological and chemical data
Provides an Introduction to basic and advanced chemoinformatics computational tools
Presents a chemical biology based toolset for artificial intelligence usage in drug design
Discusses computational methods in cancer, genome mapping, and stem cell research
Specificaties
Lezersrecensies
Inhoudsopgave
- Bridging Bioinformatics and Chemoinformatics Approaches in Public Databases
- An Introduction to Basic and Advanced Chemoinformatics Computational Tools
- An Introduction to Basic and Advanced Bioinformatics Computational Tools
- Computational Methods in Cancer, Genome Mapping and Stem Cell Research
- Using Chemistry to Understand Biology : Graphs Theories, Biological Networks
- Machine Learning and Datamining : Uses and Challenges In Bioinformatics
- Chemical Biology Based Toolset for Artificial Intelligence Usage In Drug Design
- Application of Deep Learning In Chemistry and Biology
- Text Mining in Chemistry for Organizing Chemistry Data
- CNN Use in Virtual Screening and Their Performance
- Machine Learning in Improving Force Fields of Molecular Dynamics
- Defining the Role of Chemical and Biological Data and Applying Algorithms thereof
- Optimization and Quantification of Uncertainty in Machine Learning
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