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Bioinformatics Computing

Book Description

The complete, practical guide to bioinformatics for molecular biologists and life scientists

  • Gives an overview of bioinformatics from a computer science perspective

  • Makes the computer science aspects of bioinformatics more understandable for life scientists.

  • Presents a ready reference for current and future online and standalone tools

In Bioinformatics Computing, Harvard Medical School and MIT faculty member Bryan Bergeron presents a comprehensive and practical guide to bioinformatics for life scientists at every level of training and practice. After an up-to-the-minute overview of the entire field, he illuminates every key bioinformatics technology, offering practical insights into the full range of bioinformatics applications-both new and emerging. Coverage includes:

  • Technologies that enable researchers to collaborate more effectively

  • Fundamental concepts, state-of-the-art tools, and "on the horizon" advances

  • Bioinformatics information infrastructure, including GENBANK and other Web-based resources

  • Very large biological databases: object-oriented database methods, data mining/warehousing, knowledge management, and more

  • 3D visualization: exploring the inner workings of complex biological structures

  • Advanced pattern matching techniques, including microarray research and gene prediction

  • Event-driven, time-driven, and hybrid simulation techniques

Bioinformatics Computing combines practical insight for assessing bioinformatics technologies, practical guidance for using them effectively, and intelligent context for understanding their rapidly evolving roles.

Table of Contents

  1. Copyright
  2. About Prentice Hall Professional Technical Reference
  3. Preface
  4. Acknowledgments
  5. The Central Dogma
    1. The Killer Application
    2. Parallel Universes
    3. Watson's Definition
    4. Top-Down Versus Bottom-Up
    5. Information Flow
    6. Convergence
    7. Endnote
  6. Databases
    1. Definitions
    2. Data Management
    3. Data Life Cycle
    4. Database Technology
    5. Interfaces
    6. Implementation
    7. Endnote
  7. Networks
    1. Geographical Scope
    2. Communications Models
    3. Transmissions Technology
    4. Protocols
    5. Bandwidth
    6. Topology
    7. Hardware
    8. Contents
    9. Security
    10. Ownership
    11. Implementation
    12. Management
    13. On the Horizon
    14. Endnote
  8. Search Engines
    1. The Search Process
    2. Search Engine Technology
    3. Searching and Information Theory
    4. Computational Methods
    5. Search Engines and Knowledge Management
    6. On the Horizon
    7. Endnote
  9. Data Visualization
    1. Sequence Visualization
    2. Structure Visualization
    3. User Interface
    4. Animation Versus Simulation
    5. General-Purpose Technologies
    6. On the Horizon
    7. Endnote
  10. Statistics
    1. Statistical Concepts
    2. Microarrays
    3. Imperfect Data
    4. Basics
    5. Quantifying Randomness
    6. Data Analysis
    7. Tool Selection
    8. Statistics of Alignment
    9. Clustering and Classification
    10. On the Horizon
    11. Endnote
  11. Data Mining
    1. Methods
    2. Technology Overview
    3. Infrastructure
    4. Pattern Recognition and Discovery
    5. Machine Learning
    6. Text Mining
    7. Tools
    8. On the Horizon
    9. Endnote
  12. Pattern Matching
    1. Fundamentals
    2. Dot Matrix Analysis
    3. Substitution Matrices
    4. Dynamic Programming
    5. Word Methods
    6. Bayesian Methods
    7. Multiple Sequence Alignment
    8. Tools
    9. On the Horizon
    10. Endnote
  13. Modeling and Simulation
    1. Drug Discovery
    2. Fundamentals
    3. Protein Structure
    4. Systems Biology
    5. Tools
    6. On the Horizon
    7. Endnote
  14. Collaboration
    1. Collaboration and Communications
    2. Standards
    3. Issues
    4. On the Horizon
    5. Endnote
  15. Bibliography
    1. Chapter One—The Central Dogma
    2. Chapter Two—Databases
    3. Chapter Three—Networks
    4. Chapter Four—Search Engines
    5. Chapter Five—Data Visualization
    6. Chapter Six—Statistics
    7. Chapter Seven—Data Mining
    8. Chapter Eight—Pattern Matching
    9. Chapter Nine—Modeling and Simulation
    10. Chapter Ten—Collaboration
  16. Index