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Problem Solving Handbook in Computational Biology and Bioinformatics

By Lenwood S. Heath , Naren Ramakrishnan

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Bioinformatics is constantly evolving, but a core body of algorithmic ideas for a problem-solving approach in the field has emerged. This handbook stresses that approach as it covers all relevant areas of computational biology and bioinformatics.

Full Description

  • ISBN13: 978-0-3870-9759-6
  • 368 Pages
  • User Level: Science
  • Publication Date: October 20, 2010
  • Available eBook Formats: PDF
  • eBook Price: $129.00
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Full Description
Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a 'problem solving' approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems.Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference.Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry.
Table of Contents

Table of Contents

  1. Preface.
  2. Pairwise Sequence Alignment.
  3. Sequence and Sequence Alignment Statistics.
  4. Practical Multiple Sequence Alignment.
  5. Phylogenetic Trees from Sequences.
  6. Phylogenetic Networks.
  7. Modern BLAST Programs.
  8. Stochastic Calculus and Stochastic Simulation.
  9. Genomic Sequence Comparison.
  10. Genome Rearrangements.
  11. Population Genetics Data Analysis.
  12. Genome Wide Association Studies.
  13. Practical Implications of Coalescent Theory.
  14. Networks in Computational Systems Biology.
  15. Practical Use of the Gene Ontology.
  16. Techniques for Protein Structure.
  17. Microarray Experiment Statistics.
  18. Reasoning with Protein Contact Maps .
  19. Identifying Modules in Protein
  20. Protein Interaction Networks.
  21. Motifs for Transcriptional Regulation .
  22. Time Course Analysis.
  23. Computational Simulation of Biochemical Networks.
  24. Biomedical Data Integration.
  25. Functional Annotation and Discovery.
  26. Matrix and Tensor Decompositions.
  27. Text Processing and Mining.
  28. Formal Logics.
  29. Index.
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