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Computational cancer biology : an interaction network approach

ผู้แต่ง: M Vidyasagar
สำนักพิมพ์: London ; New York : Springer, ©2012.
ชุด: SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
ครั้งที่พิมพ์/รูปแบบ:   หนังสืออีเล็กทรอนิกส์ : เอกสาร : ภาษาอังกฤษดูครั้งที่พิมพ์และรูปแบบ
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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  อ่านมากขึ้น…
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รูปแบบทางกายภาพเพิ่มเติม Print version:
Vidyasagar, M. (Mathukumalli), 1947-
Computational cancer biology.
London ; New York : Springer, ©2012
(DLC) 2012950853
ขนิดวัสดุ: เอกสาร, ทรัพยากรอินแทอร์เน็ต
ประเภทเอกสาร แหล่งข้อมูลอินเทอร์เน็ต, ไฟล์คอมพิวเตอร์
ผู้แต่งทั้งหมด : ผู้แต่งร่วม M Vidyasagar
ISBN: 9781447147510 1447147510 1447147502 9781447147503
OCLC Number: 822978249
คำอธิบาย: 1 online resource (xii, 80 pages) : illustrations.
สารบัญ: The Role of System Theory in Biology --
Analyzing Statistical Significance --
Inferring Gene Interaction Networks --
Some Research Directions.
ชื่อชุด: SpringerBriefs in electrical and computer engineering., Control, automation and robotics.
ความรับผิดชอบ: Mathukumalli Vidyasagar.

บทคัดย่อ:

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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