Biological Network Analysis
Trends, Approaches, Graph Theory, and Algorithms
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Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms considers three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN), and Human Brain Connectomes. The book's authors discuss various graph theoretic and data analytics approaches used to analyze these networks with respect to available tools, technologies, standards, algorithms and databases for generating, representing and analyzing graphical data. As a wide variety of algorithms have been developed to analyze and compare networks, this book is a timely resource.
Key Features
- Presents recent advances in biological network analysis, combining Graph Theory, Graph Analysis, and various network models
- Discusses three major biological networks, including Gene Regulatory Networks (GRN), Protein-Protein Interaction Networks (PPIN) and Human Brain Connectomes
- Includes a discussion of various graph theoretic and data analytics approaches
Readership
Students and researchers in biomedical engineering and computational biology who are interested in biological network analysis for biological modelling
Table of Contents
1. Introduction
2. Preliminaries of Graph Theory
3. Graph Analysis
4. Complex Network Models
5. Graph Databases in Bioinformatics and Computational Biology
6. Gene Regulatory Networks-Inference and Analysis
7. Protein Protein Interaction Networks
8. Brain Connectomes and Analysis
9. Conclusion
Product details
- No. of pages: 210
- Language: English
- Copyright: © Academic Press 2020
- Published: May 11, 2020
- Imprint: Academic Press
- eBook ISBN: 9780128193518
- Paperback ISBN: 9780128193501
About the Authors
Pietro Hiram Guzzi
Pietro Hiram Guzzi the Ph.D. degree in biomedical engi- neering from Magna Græcia University, Italy, in 2008. He has been an Associate Professor of computer engineering with Magna Græcia Univer- sity since 2008. He has been a Visiting Researcher with Georgia Tech University, Atlanta. He has authored two books. His research interests include semantic-based and network-based analysis of biological and clinical data. He is a member of the ACM, BITS, ISMB, and NETBIO COSI. He is an Editor of a newsletter of the ACM Special Interest Group on Bioinformatics, Computational Biology, and Biomedical Informatics (SIGBio), and the IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS. He serves the scientific community as a reviewer for many conferenceS. He wrote two books and he edited another one.
Affiliations and Expertise
Assocaite Professor of Computer Engineering, University of Magna Græcia, Catanzaro, Italy
Swarup Roy
Dr. Swarup Roy is an Associate Professor in Computer Science at Sikkim (Central) University, India. He also worked for more than 11 years as Assistant Professor at North Eastern Hill University, Shillong, India. He received his MTech and PhD (Computer Science & Engineering) from Tezpur University, India. He worked as a Post-Doctoral Fellow at the University of Colorado at Colorado Springs, USA, and Indian Institute of Technology, Guwahati, India. His research interests include data science, data mining, machine learning and its applications of data analytics to social and biological network analysis, and omics data analysis. He has served on technical committees of reputed international journals and conferences, and co-chaired the ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) 2017, Boston, USA. Dr. Roy is the author of Multi-Agent-Based Simulation XXI from Springer, Data Mining Techniques and Its Application in Medical Imagery from VDM, and Biological Network Analysis: Trends, Approaches, Graph Theory, and Algorithms from Elsevier Academic Press
Affiliations and Expertise
Associate Professor in Computer Applications, Sikkim (Central) University, India
Ratings and Reviews
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Arpita R. Sun Feb 16 2020
Biological Network Analysis
It is a wonderfully written interdisciplinary book. Can gain knowledge about the gene regulatory networks and protein-protein interactions using bioinformatics approach.