Bioinformatics for biologists
Machine generated contents note: Preface; Acknowledgements; Introduction Pavel Pevzner and Ron Shamir; Part I. Genomes: 1. Identifying the genetic basis of disease Vineet Bafna; 2. Pattern identification in a haplotype block Kun-Mao Chao; 3. Genome reconstruction: a puzzle with a billion pieces Phil...
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| Format: | UnknownFormat |
| Sprache: | eng |
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Cambridge u.a.
Cambridge University Press
2011
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| Ausgabe: | 1. publ. |
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Tag hinzufügen | Zusammenfassung: | Machine generated contents note: Preface; Acknowledgements; Introduction Pavel Pevzner and Ron Shamir; Part I. Genomes: 1. Identifying the genetic basis of disease Vineet Bafna; 2. Pattern identification in a haplotype block Kun-Mao Chao; 3. Genome reconstruction: a puzzle with a billion pieces Phillip Compeau and Pavel Pevzner; 4. Dynamic programming: one algorithmic key for many biological locks Mikhail Gelfand; 5. Measuring evidence: who's your daddy? Christopher Lee; Part II. Gene Transcription and Regulation: 6. How do replication and transcription change genomes? Andrei Grigoriev; 7. Modeling regulatory motifs Sridhar Hannenhalli; 8. How does influenza virus jump from animals to humans? Haixu Tang; Part III. Evolution: 9. Genome rearrangements Steffen Heber and Brian Howard; 10. The crisis of the tree of life concept and the search for order in the phylogenetic forest Eugene Koonin, Pere Puigb- and Yuri Wolf; 11. Reconstructing the history of large-scale genomic changes: biological questions and computational challenges Jian Ma; Part IV. Phylogeny: 12. Figs, wasps, gophers, and lice: a computational exploration of coevolution Ran Libeskind-Hadas; 13. Big cat phylogenies, consensus trees, and computational thinking Seung-Jil Sun and Tiffani Williams; 14. Algorithm design for large-scale phylogeny Tandy Warnow; Part V. Regulatory Networks: 15. Biological networks uncover evolution, disease, and gene functions Nataša Pržulj; 16. Regulatory network inference Russell Schwartz; Index. "The computational education of biologists is changing to prepare students for facing the complex datasets of today's life science research. In this concise textbook, the authors' fresh pedagogical approaches lead biology students from first principles towards computational thinking. A team of renowned bioinformaticians take innovative routes to introduce computational ideas in the context of real biological problems. Intuitive explanations promote deep understanding, using little mathematical formalism. Self-contained chapters show how computational procedures are developed and applied to central topics in bioinformatics and genomics, such as the genetic basis of disease, genome evolution or the tree of life concept. Using bioinformatic resources requires a basic understanding of what bioinformatics is and what it can do. Rather than just presenting tools, the authors - each a leading scientist - engage the students' problem-solving skills, preparing them to meet the computational challenges of their life science careers"-- |
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| Beschreibung: | Includes index |
| Beschreibung: | XXIX, 362 S. Ill., graph. Darst. 25 cm |
| ISBN: | 9781107011465 978-1-107-01146-5 9781107648876 978-1-107-64887-6 |