Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics) - Info and Reading Options
By Rongling Wu and Lin, Min.
"Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics)" is published by Chapman & Hall/CRC in July 15, 2008 - Hoboken, it has 365 pages and the language of the book is English.
“Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics)” Metadata:
- Title: ➤ Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics)
- Authors: Rongling WuLin, Min.
- Language: English
- Number of Pages: 365
- Publisher: Chapman & Hall/CRC
- Publish Date: July 15, 2008
- Publish Location: Hoboken
“Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics)” Subjects and Themes:
- Subjects: ➤ Chromosome Mapping - Genetic Markers - Mathmatical models - Methods - Pharmacogenetics - Statistical Models - Statistical methods - Biology, Life Sciences - Mathematics - Science/Mathematics - Probability & Statistics - General - Mathematics / Statistics - Pharmacology - Mathematical models - Genetic markers - Pharmacogénétique - Méthodes statistiques - Modèles mathématiques - Marqueurs génétiques - MEDICAL
Edition Specifications:
- Format: Hardcover
Edition Identifiers:
- The Open Library ID: OL12313838M - OL9251766W
- Online Computer Library Center (OCLC) ID: 297170612 - 214300065
- Library of Congress Control Number (LCCN): 2008013037
- ISBN-13: 9781584888284
- ISBN-10: 1584888288
- All ISBNs: 1584888288 - 9781584888284
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"Statistical and Computational Pharmacogenomics (Interdisciplinary Statistics)" Description:
Open Data:
Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science. Statistical and Computational Pharmacogenomics presents recent developments in statistical methodology with a number of detailed worked examples that outline how these methods can be applied. This comprehensive volume provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with a thorough unde
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