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Computational cancer biology : an interaction network approach Titelvorschau
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Computational cancer biology : an interaction network approach

Verfasser/in: M Vidyasagar
Verlag: London ; New York : Springer, ©2012.
Serien: SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
Ausgabe/Format   E-Book : Dokument : EnglischAlle Ausgaben und Formate anzeigen
Datenbank:WorldCat
Zusammenfassung:
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  Weiterlesen…
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Gattung/Form: Electronic books
Physisches Format Print version:
Vidyasagar, M. (Mathukumalli), 1947-
Computational cancer biology.
London ; New York : Springer, ©2012
(DLC) 2012950853
Medientyp: Dokument, Internetquelle
Dokumenttyp: Internet-Ressource, Computer-Datei
Alle Autoren: M Vidyasagar
ISBN: 9781447147510 1447147510 1447147502 9781447147503
OCLC-Nummer: 822978249
Beschreibung: 1 online resource (xii, 80 pages) : illustrations.
Inhalt: The Role of System Theory in Biology --
Analyzing Statistical Significance --
Inferring Gene Interaction Networks --
Some Research Directions.
Serientitel: SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
Verfasserangabe: Mathukumalli Vidyasagar.
Weitere Informationen:

Abstract:

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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