Titel: Integer Linear Programming in Computational and Systems Biology: An Entry-Level Text and Course
Verlag: Cambridge University Press
Erscheinungsjahr: 2019
Sprache: Englisch
ISBN-10: 1108421768
ISBN-13: 9781108421768
Einband: Paperback
Zustand: Very Good
Gewicht: 962 Gramm

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Integer linear programming (ILP) is a versatile modeling and optimization technique that is increasingly used in non-traditional ways in biology, with the potential to transform biological computation. However, few biologists know about it. This how-to and why-do text introduces ILP through the lens of computational and systems biology. It uses in-depth examples from genomics, phylogenetics, RNA, protein folding, network analysis, cancer, ecology, co-evolution, DNA sequencing, sequence analysis, pedigree and sibling inference, haplotyping, and more, to establish the power of ILP. This book aims to teach the logic of modeling and solving problems with ILP, and to teach the practical 'work flow' involved in using ILP in biology. Written for a wide audience, with no biological or computational prerequisites, this book is appropriate for entry-level and advanced courses aimed at biological and computational students, and as a source for specialists. Numerous exercises and accompanying software (in Python and Perl) demonstrate the concepts.

�ber die Autorin bzw. den Autor

Dan Gusfield is Distinguished Professor of Computer Science at the University of California, Davis, and a Fellow of the IEEE, the ACM, and the International Society of Computational Biology (ISCB). His previous books include The Stable Marriage Problem (1989, with Robert W. Irving), Algorithms on Strings, Trees and Sequences (Cambridge, 1997) and ReCombinatorics (2014). He has served as chair of the computer science department at UCD (2000–04), and was the founding Editor-in-Chief of the IEEE/ACM Transactions of Computational Biology and Bioinformatics until January 2009. He has been instrumental in the definition and development of the intersection between computer science and computational biology.

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