Bioinformatics Problem Solving Paradigms /

• Κύριος συγγραφέας: Sperschneider, Volker (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Θέματα: Computer science.; Algorithms.; Bioinformatics.; Computer Science.; Computational Biology/Bioinformatics.; Algorithm Analysis and Problem Complexity.

 There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics (e.g. sequencing and mapping, string storage and manipulation, pattern matching, alignment, gene identification, genome rearrangement, structure prediction, regulatory networks, pseudoknot detection). Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background. This book highlights basic paradigms of problem analysis and algorithm design in the context of core bioinformatics problems. Mathematically demanding themes are put across to the reader by properly chosen representations with the aid of lots of illustrations. (Prof. Dr. Amitava Datta, UWA, Perth, Australia) Written by a theoretical computer scientist, the book perfectly complements standard textbooks from the point of view of problem solving methods continuously applied in bioinformatics. (Prof. Dr. Georg Fuellen, Univ. Greifswald, Germany). 

 Core Bioinformatics Problems -- Turning to Algorithmic Problems -- Dynamic Programming -- Intelligent Data Structures -- NP-Hardness of Core Bioinformatics Problems -- Approximation Algorithms -- A Selection of Metaheuristics and Various Projects.