Computational Cancer Biology
Subtitle: An Interaction Network Approach
By: by Mathukumalli Vidyasagar
Contributors: SpringerLink (Online service)
Series: SpringerBriefs in Electrical and Computer Engineering
Publisher: Springer London; Imprint: Springer
Published: 2012
Place of publication: London
Language: eng
Format: [electronic resource] :
Number of pages: 80
ISBN-13: 9781447147510
Subjects: Control; Oncology; Statistics; Cancer; Biological models; Statistics for Life Sciences, Medicine, Health Sciences; Research; Computational Biology/Bioinformatics; Cellular and Medical Topics Physiological; Computer science; Bioinformatics; Systems biology; Mathematics; Physiology
Description: <p>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.</p><p> </p><p>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.</p><p> </p><p>After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.</p>
Table of contents: title: Introduction; title: Inferring Genetic Regulatory Networks; title: Context-specific Genomic Networks; title: Analyzing Statistical Significance; title: Separating Drivers from Passengers; title: Some Research Directions.