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Computational Cancer Biology : An Interaction Network Approach / by Mathukumalli Vidyasagar.
- Format:
- Book
- Author/Creator:
- Vidyasagar, M. (Mathukumalli), 1947- author.
- Series:
- Computer Science (Springer-11645)
- SpringerBriefs in electrical and computer engineering. Control, automation and robotics 2192-6786
- SpringerBriefs in Control, Automation and Robotics, 2192-6786
- Language:
- English
- Subjects (All):
- Bioinformatics.
- Biomathematics.
- Automatic control.
- Statistics.
- Systems biology.
- Cancer--Research.
- Cancer.
- Computational Biology/Bioinformatics.
- Physiological, Cellular and Medical Topics.
- Control and Systems Theory.
- Statistics for Life Sciences, Medicine, Health Sciences.
- Systems Biology.
- Cancer Research.
- Local Subjects:
- Computational Biology/Bioinformatics.
- Physiological, Cellular and Medical Topics.
- Control and Systems Theory.
- Statistics for Life Sciences, Medicine, Health Sciences.
- Systems Biology.
- Cancer Research.
- Physical Description:
- 1 online resource (XII, 80 pages) : 11 illustrations in color.
- Edition:
- First edition 2012.
- Contained In:
- Springer eBooks
- Place of Publication:
- London : Springer London : Imprint: Springer, 2012.
- System Details:
- text file PDF
- Summary:
- This brief introduces readers to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics, building on only a basic background in these two topics. Aside from providing a self-contained introduction to several aspects of basic biology and to cancer, as well as to the techniques from statistics most commonly used in cancer biology, the brief describes several methods for inferring gene interaction networks from expression data, including one that is reported for the first time in the brief. The application of these methods is illustrated on actual data from cancer cell lines. Some promising directions for new research are also discussed. After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.
- Contents:
- Introduction
- Inferring Genetic Regulatory Networks
- Context-specific Genomic Networks
- Analyzing Statistical Significance
- Separating Drivers from Passengers
- Some Research Directions.
- Other Format:
- Printed edition:
- ISBN:
- 978-1-4471-4751-0
- 9781447147510
- Access Restriction:
- Restricted for use by site license.
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