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Cover image for book Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools

Emerging Trends in Computational Biology, Bioinformatics, and Systems Biology: Algorithms and Software Tools

By:Tran, Quoc Nam; Arabnia, Hamd
Publisher:Elsevier S & T
Print ISBN:9780128025086
eText ISBN:9780128026465
Edition:0
Format:Reflowable

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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.

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