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Computational cancer biology : an interaction network approach

Auteur : M Vidyasagar
Éditeur : London ; New York : Springer, ©2012.
Collection : SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
Édition/format :   Livre électronique : Document : EnglishVoir toutes les éditions et tous les formats
Base de données :WorldCat
Résumé :
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  Lire la suite...
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Détails

Genre/forme : Electronic books
Format – détails additionnels : Print version:
Vidyasagar, M. (Mathukumalli), 1947-
Computational cancer biology.
London ; New York : Springer, ©2012
(DLC) 2012950853
Type d’ouvrage : Document, Ressource Internet
Format : Internet Resource, Computer File
Tous les auteurs / collaborateurs : M Vidyasagar
ISBN : 9781447147510 1447147510 1447147502 9781447147503
Numéro OCLC : 822978249
Description : 1 online resource (xii, 80 pages) : illustrations.
Contenu : The Role of System Theory in Biology --
Analyzing Statistical Significance --
Inferring Gene Interaction Networks --
Some Research Directions.
Titre de collection : SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
Responsabilité : Mathukumalli Vidyasagar.
Plus d’informations :

Résumé :

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.

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