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Problem Solving Handbook in Computational Biology and Bioinformatics [Hardcover]

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  • Category: Books (Science)
  • ISBN-10:  0387097597
  • ISBN-10:  0387097597
  • ISBN-13:  9780387097596
  • ISBN-13:  9780387097596
  • Publisher:  Springer
  • Publisher:  Springer
  • Pages:  347
  • Pages:  347
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Mar-2010
  • Pub Date:  01-Mar-2010
  • SKU:  0387097597-11-SPRI
  • SKU:  0387097597-11-SPRI
  • Item ID: 100863304
  • List Price: $109.99
  • Seller: ShopSpell
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  • Delivery by: Jul 04 to Jul 06
  • Notes: Brand New Book. Order Now.

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.

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.

Preface.- Pairwise Sequence Alignment.- Sequence and Sequence Alignment Statistics.- Practical Multiple Sequence Alignment.- Phylogenetic Trees from Sequences.- Phylogenetic Networks.- Modern BLAST Programs.- Stochastic Calculus and Stochastic Simulation.- Genomic Sequence Comparison.- Genome Rearrangements.- Population Genetics Data Analysis.- Genome Wide Association Studies.- Practical Implications of Coalescent Theory.- Networks in Computational Systems Biology.- Practical Use of the Gene Ontology.- Techniques for Protein Structure.- Microarray Experiment Statistics.- Reasoning with Protein Contact Maps .- Identifying Modules in Protein-Protein Interaction Networks.- MlcA

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