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๐Ÿ“

Bioinformatics: Sequence Alignment and Markov Models

โœ Scribed by Kal Sharma


Publisher
McGraw-Hill Professional
Year
2008
Tongue
English
Leaves
337
Edition
1
Category
Library

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โœฆ Synopsis


GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURY


Bioinformatics showcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of suffix trees, plus late-breaking advances regarding biochips and genomes.

Featuring helpful gene-finding algorithms, Bioinformatics offers key information on sequence alignment, HMMs, HMM applications, protein secondary structure, microarray techniques, and drug discovery and development. Helpful diagrams accompany mathematical equations throughout, and exercises appear at the end of each chapter to facilitate self-evaluation.

This thorough, up-to-date resource features:

  • Worked-out problems illustrating concepts and models
  • End-of-chapter exercises for self-evaluation
  • Material based on student feedback
  • Illustrations that clarify difficult math problems
  • A list of bioinformatics-related websites

Bioinformatics covers:

  • Sequence representation and alignment
  • Hidden Markov models
  • Applications of HMMs
  • Gene finding
  • Protein secondary structure prediction
  • Microarray techniques
  • Drug discovery and development
  • Internet resources and public domain databases

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Bioinformatics: sequence alignment and M
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Hidden Markov Models of Bioinformatics
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Foreword. 1. Prerequisites in probability calculus. 2. Information and the Kullback Distance. 3. Probabilistic Models and Learning. 4. EM Algorithm. 5. Alignment and Scoring. 6. Mixture Models and Profiles. 7. Markov Chains. 8. Learning of Markov Chains. 9. Markovian Models for DNA sequences.

Handbook of Hidden Markov Models in Bioi
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Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs). The book begins with discussions on