Computational Cancer Biology [electronic resource] : An Interaction Network Approach /
Vidyasagar, Mathukumalli.author.
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.
Description based upon print version of record.
Includes bibliographical references.
Introduction -- Inferring Genetic Regulatory Networks -- Context-specific Genomic Networks -- Analyzing Statistical Significance -- Separating Drivers from Passengers -- Some Research Directions.
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.
Bioinformatics.
Biomathematics.
Control engineering.
Statistics .
Systems biology.
Cancer research.
Computational Biology/Bioinformatics.
Physiological, Cellular and Medical Topics.
Control and Systems Theory.
Statistics for Life Sciences, Medicine, Health Sciences.
Systems Biology.
Cancer Research.