Morgan Kaufmann Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools (Emerging Trends in Computer Science and Applied Computing)

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology discusses the latest in all aspects of computational biology, bioinformatics, and systems biology and the application of data-analytical and algorithms, mathematical modeling and simulation techniques. Part I: Computational Biology discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques. Part II: Bioinformatics: Databases, Data Mining and Pattern Discovery focuses on how to use methods for storing, retrieving, organizing and analyzing biological data which are fundamentally extensions of techniques used in computing. Part III: Systems Biology explains how to obtain, integrate and analyze complex data sets from multiple experimental sources using interdisciplinary tools while taking into consideration the evolving nature of the field. Part IV: Big Data and Data Analytics in Computational Biology and Informatics presents strategies and techniques using robust Big Data tools for dealing with the collection of data sets so large and complex that they are difficult to process using conventional database management systems or traditional data processing applications. * Discusses the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological and behavioral systems.* Presents a systematic approach for storing, retrieving, organizing and analyzing biological data using software tools with applications. * Provides a systems biology perspective including general guidelines and techniques for obtaining, integrating and analyzing complex data sets from multiple experimental sources using computational tools and software. Review "This is a valuable resource for students, clinicians, and researchers who wish to keep abreast of the emerging trends in computational biology, bioinformatics, and systems biology.  Score: 76 - 3 Stars" -- Doody's Review An overview of computational biology, bioinformatics and system biology from a computer science perspective, emphasizing Big Data using a data-analytical driven approach About the Author Professor Quoc-Nam Tran is currently Chair and Full Professor of Computer Science at University of South Dakota. He previously served as Chair and Full Professor of Computer Science at the University of Texas at Tyler. His previous positions include: Professor of Computer Science at Lamar University; Visiting Professor at Rice University; Scientist at Wolfram Research, Champaign-Urbana; and Assistant Professor at University of Linz (Linz, Austria). Professor Tran's research interests include: computational methods and algorithmic foundations; theory of Groebner bases; bioinformatics and computational biology. He has published extensively in his areas of expertise. He has co-edited a number of books, including: "Software Tools and Algorithms for Biological Systems" (2011) and "Advances in Computational Biology" (2010) (Springer) Professor Tran has served on a number of editorial boards and has organized and chaired sessions for premier conferences such as the IEEE International Conference on Bioinformatics and Biomedicine Workshop. Hamid R. Arabnia is currently a Full Professor of Computer Science at University of Georgia where he has been since October 1987. His research interests include Parallel and distributed processing techniques and algorithms, interconnection networks, and applications in Computational Science and Computational Intelligence (in particular, in image processing, medical imaging, bioinformatics, and other computational intensive problems). Dr. Arabnia is Editor-in-Chief of The Journal of is Associate Editor of IEEE Transactions on Information Technology in Biomedicine . He has over 300 publications (journals, proceedings, editorship) in his area of research in additi