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Large Scale Data Handling in Biology

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Language:  English
Data Handling in Biology--the application of computational and analytical methods to biological problems--is a rapidly evolving scientific discipline.
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Data Handling in Biology--the application of computational and analytical methods to biological problems--is a rapidly evolving scientific discipline. Written in a clear, engaging style, Large Scale Data Handling in Biology is for scientists and students who are learning computational approaches to biology. The book covers the data storage system, computational approaches to biological problems, an introduction to workflow systems, data mining, data visualization, and tips for tailoring existing data analysis software to individual research needs.

  1. What to Do with All the Data?
  2. Data Storage, Backup and Archiving Architecture
    1. Organization of HCS Informatics Infrastructure
    2. Hardware and Network Infrastructure
    3. Do we need robust Data Movers (DM) in High Content Screening for data-flow automation
  3. Workflow Systems
    1. Why is a workflow system important
    2. Visualization in workflow systems
    3. Architecture of workflow systems
    4. Public Domain Workflow Systems
    5. Commercial Workflow Systems
    6. Summary and Vision
  4. Database Development: Laboratory Information Management Systems and Public Databases
    1. What Type of HCS Data Have to Be Managed in the Database
    2. Database Schema
    3. LIMS Architecture
    4. LIMS and User Management System
    5. Type of Users
    6. Integration and Public Databases
  5. References
About the Author

Karol Kozak

Karol Kozak has been influential in the development of InMemory Image Databases for Life Science and data mining tools for High Throughput, High Content Screening (HCS) over the few years working at Max Planck Institute of Molecular Cell Biology and Genetic in Dresden (Germany) and ETH Zurich (Switzerland). Currently, he is in charge of Biomedical Computer Science at Fraunhofer Society and Uniklinikum Dresden. He plays a leading role in defining the strategy for organizing management large scale biomedical image data. In addition, during PhD time Karol Kozak became an expert in statistical pattern recognition applied to large scale screening data.