4 edition of Methods of Microarray Data Analysis found in the catalog.
January 15, 2002
Written in English
|Contributions||Simon M. Lin (Editor), Kimberly F. Johnson (Editor)|
|The Physical Object|
|Number of Pages||189|
Microarray Data Analysis. Microarray data sets are commonly very large, and analytical precision is influenced by a number of variables. So it is extremely useful to reduce the dataset to Cited by: In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. Information on an array of topics is included in this innovative book Brand: Michael J. Korenberg.
• Causton HC et al. Microarray Gene Expression Data Analysis: A Beginner’s Guide. Blackwell, • Speed, T. (ed.) Statistical Analysis of Microarray Data. Chapman & Hall, • Smyth GK et al. File Size: KB. AI Methods for Analyzing Microarray Data: /ch Biological systems can be viewed as information management systems, with a basic instruction set stored in each cell’s DNA as Author: Amira Djebbari, Aedín C. Culhane, Alice J. Armstrong, John Quackenbush.
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This volume covers a large area, from the description of methodologies for data analysis to the real application. Chapters focus on methodologies for preprocessing of microarray data, a survey of.
Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, 3/5(1).
This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used.
From the contents:. Methods of Microarray Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on Format: Hardcover. METHODS OF MICROARRAY DATA ANALYSIS IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint.
Previous books in this series. Request PDF | Methods of Microarray Data Analysis | Recent literature regarding microarray technology has focused on the need to incorporate classical statistical practices in experimental design.
Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In Methods of Microarray Data Analysis book single reference, readers can learn about the most up-to-date methods ranging from data. Microarray Data Analysis is called expression ratio.
It is denoted here as Tk and defi ned as: and defi ned as: k Tk = Rk G For each gene k on the array, where on the array, where Rk represents the spot.
Methods of Microarray Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on. In comparison with previous methods of gene expression analysis such as dot, slot and northern blots, the emergence of microarrays was an extraordinary advancement and provided the ability to.
Buy Microarray Data Analysis (): Methods and Applications: NHBS - Edited By: Michael Korenberg, Humana Press. Microarray analysis techniques are used in interpreting the data generated from experiments on DNA (Gene chip analysis), RNA, and protein microarrays, which allow researchers to investigate the.
Get this from a library. Microarray data analysis: methods and applications. [Michael J Korenberg;] -- "In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis.
This innovative book. ISBN: OCLC Number: Notes: "Papers from CAMDA '" Description: xiv, pages: illustrations ; 24 cm: Contents: Data Mining and Machine Learning.
Satomi Miwa, Alan Cohen, in Handbook of Models for Human Aging, Microarray Analysis. Microarray analysis is a method that makes use of gene chips to which thousands of different mRNAs. The major drawback in microarray data is the “curse of dimensionality problem,” which hinders the useful information of a data set and leads to computational instability.
Therefore, selecting relevant genes is. of standard statistical methods and a lesser knowledge of related topics such as molecular biology or bioinformatics. One problem for many statisticians considering to start working on mi-croarray data File Size: 1MB.
A great introductory book that details reliable approaches to problems met in standard microarray data analyses. It provides examples of established approaches such as cluster analysis, function.
• Gene data can be “translated” into IDs from a wide variety of sequence databases: – LocusLink, Ensembl, UniGene, RefSeq, genome databases – Each database in turn links to a lot of different File Size: 1MB. Book Description. Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community.
Delivering a detailed discussion of the biological aspects and applications of microarrays, the book. Methods in Microarray Normalization provides scientists with a complete resource on the most effective tools available for maximizing microarray data in biochemical research.
Daniel E. Levy, editor of the .Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods.
"Analysis for gene expression data is the latest hot new topic in statistical data analysis [this book] deals with microarray experiments: design and analysis for a comparative study, classification methods for data analysis, and clustering for data analysis.
Scientists whose work concerns this type of data will want to get a copy of the book.".