Last edited by Shakasida
Thursday, August 6, 2020 | History

6 edition of Analysis of Microarray Gene Expression Data (Trends in Logic) found in the catalog.

Analysis of Microarray Gene Expression Data (Trends in Logic)

by Mei-Ling Ting Lee

  • 326 Want to read
  • 0 Currently reading

Published by Springer .
Written in English

    Subjects:
  • DNA,
  • Science,
  • Life Sciences - Genetics & Genomics,
  • Science/Mathematics,
  • Genetics,
  • Life Sciences - Biochemistry,
  • Probability & Statistics - General,
  • Mathematics / Statistics,
  • Biostatistics,
  • Research

  • The Physical Object
    FormatHardcover
    Number of Pages400
    ID Numbers
    Open LibraryOL7809486M
    ISBN 100792370872
    ISBN 109780792370871

    GEO2R is a great tool to browse expression data experiments. It does a simple version of ANOVA and will really help you find some cool biology quickly. VAMPIRE is a web analysis suite for . Microarray technology is a powerful tool for the simultaneous analysis of the expression of thousands of genes in tissues, organs, or cells. DNA sequence information encoding RNA for .

      Timeliness. Last modified on 11 August Best feature. The major use of the site in its present form is to provide current links and detailed information on the use and Author: Paul Kellam. DNA MICROARRAYS AND GENE EXPRESSION From experiments to data analysis and modeling Massive data acquisition technologies, such as genome sequencing, high .

    Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in bioinformatics : Ju Han Kim. Statistical Analysis of Gene Expression Microarray Data book. Read reviews from world’s largest community for readers. Although less than a decade old, t /5(4).


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Analysis of Microarray Gene Expression Data (Trends in Logic) by Mei-Ling Ting Lee Download PDF EPUB FB2

This book attempts to explain the underlying concepts and principles routinely used in analysis of gene expression data. The book should be accessible by statisticians, computer scientists, 4/5(2).

analysis of microarray gene expression data Download analysis of microarray gene expression data or read online books in PDF, EPUB, Tuebl, and Mobi Format. Click Download or Read Online button to get analysis of microarray gene expression data book.

This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources.

the expression levels of thousands of genes simultaneously under a particular condition, called gene expression analysis. Microarray technology makes this possible and the quantity of data. A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available.

‘The Analysis of Gene Expression Data: Methods and Software’ appears as a successful attempt. this book succeeds in the not trivial task of providing an informative, accessible, overall. Gene expression microarrays provide a snapshot of all the transcriptional activity in a biological sample.

Unlike most traditional molecular biology tools, which generally allow the study of a Cited by: With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis.

The contribution of this book. Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field. Under the editorship of Terry Speed, some of the world's most pre Cited by:   • Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets • Sample.

In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. Included in this innovative book includes are in-depth looks intopresentations of.

Analysis of microarray gene expression data. [Mei-Ling Ting Lee] "This book aims to be a comprehensive work on statistical techniques for analysis of microarray data. the book. --Confirming Microarray Results --Northern Blot Analysis --Reverse-transcription PCR and Quantitative Real-time RT-PCR --Inherent Variability in Array Data --Genetic Populations.

DNA microarray technology has led to rapid accumulation of gene expression data generated in the last several years. GEO serves as a unifying resource for these data, operating primarily Cited by: Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field.

Under the editorship of Terry Speed, some of the world's most pre. * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book. • Causton HC et al. Microarray Gene Expression Data Analysis: A Beginner’s Guide.

Blackwell, • Churchill, GA. Fundamentals of experimental design for cDNA microarrays. Nature. Dupuy and Simon developed guidelines for the analysis of DNA microarray data in conjunction with outcomes of cancer patients, illustrated by a list of Do's and Don'ts.

BRB-ArrayTools Cited by: Analysis of Microarray Data Lecture 3: Visualization and Functional Analysis George Bell, Ph.D. Senior Bioinformatics Scientist Bioinformatics and Research ComputingFile Size: 1MB.

An evaluation of image analysis methods for spotted cDNA arrays was reported by Yang et al. [8]. For a microarray project, the image quantification marks the transition in the work flow from. A DNA microarray (also commonly known as DNA chip or biochip) is a collection of microscopic DNA spots attached to a solid ists use DNA microarrays to measure the .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 .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 .