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Statistical Methods for Microarray Data Analysis Methods and Protocols [Hardcover]

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  • Category: Books (Medical)
  • ISBN-10:  1603273360
  • ISBN-10:  1603273360
  • ISBN-13:  9781603273367
  • ISBN-13:  9781603273367
  • Publisher:  Humana
  • Publisher:  Humana
  • Pages:  212
  • Pages:  212
  • Binding:  Hardcover
  • Binding:  Hardcover
  • Pub Date:  01-Mar-2013
  • Pub Date:  01-Mar-2013
  • SKU:  1603273360-11-SPRI
  • SKU:  1603273360-11-SPRI
  • Item ID: 100890413
  • List Price: $109.99
  • Seller: ShopSpell
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Microarrays for simultaneous measurement of redundancy? of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular Biology series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory.

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Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study? microarrays and the most current statistical methods.

1. What Statisticians Should Know About Microarray Gene Expression Technology

Stephen Welle

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2. Where Statistics and Molecular Microarray Experiments Biology Meet

Diana M. Kelmansky

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3. Multiple Hypothesis Testing: A Methodological Overview

Anthony Almudevar

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4. Gene Selection with the d-sequence Method

Xing Qiu and Lev B Klebanov

?5. Using of Normalizations for Gene Expression Analysis???

Peter Bubel?ny

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6. Constructing Multivariate Prognostic Gene Signatures with Censored Survival Data

Derick R. Peterson

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7. Clustering of Gene-Expression Data via Normal Mixture Models