Leseprobe
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Pattern Recognition in Computational Molecular Biology
Jason T. L. Wang, Mourad Elloumi, Albert Y. Zomaya, et al.
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Naturwissenschaften, Medizin, Informatik, Technik / Elektronik, Elektrotechnik, Nachrichtentechnik
Beschreibung
A comprehensive overview of high-performance pattern recognition techniques and approaches to Computational Molecular Biology This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks. * Surveys the development of techniques and approaches on pattern recognition in biomolecular data * Discusses pattern recognition in primary, secondary, tertiary and quaternary structures, as well as microarrays, phylogenetic trees and biological networks * Includes case studies and examples to further illustrate the concepts discussed in the book Pattern Recognition in Computational Molecular Biology: Techniques and Approaches is a reference for practitioners and professional researches in Computer Science, Life Science, and Mathematics. This book also serves as a supplementary reading for graduate students and young researches interested in Computational Molecular Biology.
Kundenbewertungen
Biowissenschaften, Statistics, Electrical & Electronics Engineering, Mustererkennung, Bioinformatics & Computational Biology, Pattern Analysis, Life Sciences, Molekularbiologie, Statistik, Elektrotechnik u. Elektronik, Bioinformatik u. Computersimulationen in der Biowissenschaften, Data Mining, Bioinformatik, Data Mining Statistics