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Introduction to Computational Genomics: A Case Studies Approach
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Introduction to Computational Genomics: A Case Studies Approach Paperback - 2006

by Nello Cristianini; Matthew W. Hahn

This readable and entertaining book provides a roadmap to navigate entry to the field of computational genomics in biology. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding, and other topics are introduced in a rigorous yet accessible way.


From the publisher

Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.

Details

  • Title Introduction to Computational Genomics: A Case Studies Approach
  • Author Nello Cristianini; Matthew W. Hahn
  • Binding Paperback
  • Edition [ Edition: repri
  • Pages 202
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press, USA
  • Date 2006-12-14
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Table of Contents, Textbook
  • ISBN 9780521671910 / 0521671914
  • Weight 0.9 lbs (0.41 kg)
  • Dimensions 9.6 x 7.3 x 0.8 in (24.38 x 18.54 x 2.03 cm)
  • Library of Congress subjects Computational Biology - methods, Computational biology
  • Library of Congress Catalog Number 2007273397
  • Dewey Decimal Code 572.860
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Introduction to Computational Genomics: A Case Studies Approach
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Introduction to Computational Genomics: A Case Studies Approach

by Nello Cristianini; Matthew W. Hahn

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Introduction to Computational Genomics: A Case Studies Approach
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Introduction to Computational Genomics: A Case Studies Approach

by Nello Cristianini/ Matthew W. Hahn

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Introduction to Computational Genomics: A Case Studies Approach

Introduction to Computational Genomics: A Case Studies Approach

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Paperback / softback. New. Are we related to Neanderthals? Where did SARS come from? Such questions are answered by statistical and computational analysis of genetic data, and are the vehicle for this entertaining introduction to computational genomics. The case-studies approach with Matlab demos provides an enjoyable way to learn how to analyze genomic data first-hand.
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Introduction to Computational Genomics: A Case Studies Approach
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Introduction to Computational Genomics: A Case Studies Approach

by Cristianini, Nello

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ISBN: 9780521671910
Cambridge University Press | 01 January 2006
Paperback | 202 pages
Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the… Read More
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