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Bioinformatics: problem solving paradigms

By: Sperschneider, Volker.
Contributor(s): Sperschneider, Jana [Contributor] | Scheubert, Lena [Contributor ].
Publisher: New York Springer 2008Description: xviii, 289 p.ISBN: 9783540785057.Subject(s): BioinformaticsDDC classification: 570.285 Summary: 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.
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Books Vikram Sarabhai Library
Slot 1701 (2 Floor, East Wing) 570.285 S7B4 (Browse shelf) Available 166760

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.

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