Artificial Intelligence in Bioinformatics and Chemoinformatics

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

1. Bridging Bioinformatics and Chemoinformatics Approaches in Public Databases
2. An Introduction to Basic and Advanced Chemoinformatics Computational Tools
3. An Introduction to Basic and Advanced Bioinformatics Computational Tools
4. Computational Methods in Cancer, Genome Mapping and Stem Cell Research
5. Using Chemistry to Understand Biology : Graphs Theories, Biological Networks
   
6. Machine Learning and Datamining : Uses and Challenges In Bioinformatics
   
7. Chemical Biology Based Toolset for Artificial Intelligence Usage In Drug Design
   
8. Application of Deep Learning In Chemistry and Biology
   
9. Text Mining in Chemistry for Organizing Chemistry Data
   
10. CNN Use in Virtual Screening and Their Performance
11. Machine Learning in Improving Force Fields of Molecular Dynamics
    
12. Defining the Role of Chemical and Biological Data and Applying Algorithms thereof
    
13. Optimization and Quantification of Uncertainty in Machine Learning

Dr. Yashwant Pathak has over 9 years of versatile administrative experience in an academic setting as Dean (and over 17 years as faculty and as a researcher in higher education after his Ph.D) and has worked in student admission, academic affairs, research, graduate programs, Chair of the International Working group for USF Health (consisting of USF Medical, Nursing, Public Health, Pharmacy and Physiotherapy colleges), Director of Nano Center and now holds the position for Associate Dean for Faculty Affairs. Dr. Yash Pathak is an internationally recognized scholar, researcher and educator in the areas of Health care education, Nanotechnology, Drug Delivery Systems and Nutraceuticals. Dr Pathak has edited several books in the field of Nanotechnology (Springer) and Drug Delivery Systems (CRC Press), Antibody mediated drug delivery systems (John Wiley and Sons) and in the field of Nutraceuticals (Taylor and Francis), Artificial neural network(Elsevier) and conflict resolution and cultural studies, with over 300 research publications, reviews, chapters, abstracts and articles in educational research. He has authored many popular articles published in newspapers and magazines. He fluently speaks many languages and was trained and taught in English all through his career.

Dr. Surovi Saikia works as a Dr. DS Kothari Postdoctoral Fellow at the Department of Biotechnology, Bharathiar University, Coimbatore. She has completed her Ph.D (Bioinformatics) from Dibrugarh University, Assam in 2020 and UG and PG from Tinsukia college and Dibrugarh University. She has 8 years of research experience with 18 publications in peer reviewed national and international journals. Currently, she is working on algorithmic based computational work.

Research Experience

• Dr. D.S Kothari Postdoctoral Fellow at Bharathiar University, Coimbatore, Tamil Nadu from 23/11/2021 till date.
• Senior Research Fellow-NFST Fel