Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers

by Sudan Jha PhD (Editor), Sultan Ahmad PhD. (Editor), Subhendukumar Pani (Editor)

Overview

Harnessing Artificial Intelligence-Enhanced Graph Models for Biological Discovery: Unveiling Biological Frontiers introduces revolutionary techniques that merge artificial intelligence with graph-based methods to uncover complex biological networks. Through detailed examples and case studies, the book provides researchers and practitioners the essential tools to analyze molecular interactions, identify key biomarkers, and hasten the discovery of novel therapeutics. Chapters delve into the sophisticated interplay between advanced AI techniques and graph models, specially designed to decode the intricacies of biological systems. By utilizing cutting-edge AI algorithms, readers can explore complex biological networks, forecast molecular interactions, and pinpoint new drug targets with exceptional precision.

Product Details

ISBN-13:	9780443276088
Publisher:	Elsevier Science
Publication date:	11/01/2025
Pages:	220
Product dimensions:	7.50(w) x 9.25(h) x 0.00(d)

About the Author

Sudan Jha holds a Ph.D. in CSE and Ph.D. in CE*, with 18 years of experience in Academia, Administration as a Principal in four Colleges in India and Nepal, and Industrial Research and Software Project Development & Management. As a team leader, Dr Jha executed three government-funded projects; sat on the Board of Directors and acted as Technical Advisor for Nepal's National Television (NTV); Chief IT Consultant in Nepal Telecom Authority, the regulating body for telecommunications in Nepal. Dr Jha has published 31 International Journal transaction papers [SCOPUS Indexed-3, UGC Approved-8, Others-20]; 30+ conference papers; 8 major software developed as Team Leader including Govt and non Govt. organization, and are a Editorial board member for several Journals, International Conferences, and books.

Sultan Ahmad (Member, IEEE) is currently a Faculty Member of the Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia. He earneda Master of Computer Science and Applications from Aligarh Muslim University, India, in 2006, and a Ph.D. degree in CSE from Glocal University. He has almost 2 decades of teaching and research experience. He has more than 110 accepted and published research papers and book chapters in reputed SCI, SCIE, ESCI, and SCOPUS-indexed journals and conferences. He has an Australian patent and a Chinese patent in his name. He has authored several books and presented his research papers at many national and international conferences. His research interests include Intelligent computing, data Science, machine learning, and the Internet of Things. He is a member of IEEE, IACSIT and the Computer Society of India

Dr. Subhendu kumar Pani received his Ph.D. from Utkal University ,Odisha, India. He is working as professor at Krupajal Engineeing College under BPUT, Odisha, India. He has almost 2 decades of teaching and research experience. His research interests include Data mining, Big Data Analysis, web data analytics, Fuzzy Decision Making and Computational Intelligence. He is the recipient of 5 researcher awards. He has published dozens of international journal papers. His professional activities include roles as Book Series Editor, Associate Editor, Editorial board member and/or reviewer of various international journals. He is Associated with a number of conference societies. He has more than 150 international publications, 5 authored books, 15 edited and upcoming books; 20 book chapters into his account. He is a fellow in SSARSC and life member in IE, ISTE, ISCA,OBA.OMS, SMIACSIT, SMUACEE, CSI.

Table of Contents

1. Introduction to AI-Powered Graph Models in Biology
2. Fundamentals of Graph Theory and Biological Networks
3. Graph-Based Machine Learning Techniques
4. Graph Models for Genomics Data Analysis
5. Network Medicine and Disease Prediction
6. Drug Discovery and Re-purposing using Graph Models
7. Graph-Based Analysis of Biological Pathways
8. Case Studies and Applications in Biomedical Research
9. Challenges and Future Directions in AI-Powered Graph Models for Biology

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From the Publisher

Presents a comprehensive exploration of cutting-edge research at the intersection of artificial intelligence (AI) and biological sciences