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Download Analysis of Microarray Data: A Network-Based Approach by Frank Emmert-Streib, Matthias Dehmer PDF

By Frank Emmert-Streib, Matthias Dehmer

This ebook is the 1st to target the appliance of mathematical networks for reading microarray info. this technique is going well past the normal clustering equipment typically used.

From the contents:

  • Understanding and Preprocessing Microarray info
  • Clustering of Microarray facts
  • Reconstruction of the Yeast cellphone Cycle by means of Partial Correlations of upper Order
  • Bilayer Verification set of rules
  • Probabilistic Boolean Networks as versions for Gene rules
  • Estimating Transcriptional Regulatory Networks by means of a Bayesian community
  • Analysis of healing Compound results
  • Statistical tools for Inference of Genetic Networks and Regulatory Modules
  • Identification of Genetic Networks via Structural Equations
  • Predicting practical Modules utilizing Microarray and Protein interplay information
  • Integrating effects from Literature Mining and Microarray Experiments to deduce Gene Networks

The booklet is for either, scientists utilizing the process in addition to these constructing new research recommendations.

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Additional resources for Analysis of Microarray Data: A Network-Based Approach

Sample text

In terms of sensitivity, specificity, and reproducibility, a large comprehensive study recently indicated that both single and double sample platforms are equivalent [97]. 1 Be Unbiased, Be Complete Microarrays afford a new luxury that changes the way science is done. Just a short time ago, the measurement of gene expression was performed one or two genes at a time, and almost always with a hypothesis about the gene in mind. These days, however, this would be considered a biased approach. Since microarrays confer the ability to measure virtually all the genes, one can take an unbiased approach to a problem by examining all genes and identifying those genes or elements that exhibit change under some condition.

In addition, the hybridization characteristics of a DNA probe are unique and sequence dependent. Each type of array that utilizes hybridization as a readout suffers from the effects of hybridization differences between DNA sequences representing genes, and measures are taken to minimize them. Affymetrix and NimbleGen address this problem by using multiple probes to represent a gene. For spotted arrays the problem of hybridization differences between probes is less of an issue because comparisons are not made on a gene-by-gene basis within a sample.

Chapter 4. C. H. (2001) Issues in cDNA microarray analysis: quality filtering channel normalization, models of variations and assessment of gene effects. Nucleic Acids Research, 29 (12), 2549–2557. M. K. (2007) Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes. Genome Biology, 8 (1), R2. S. B. (2007) Assessing the need for sequence-based normalization in tiling microarray experiments. Bioinformatics, 23 (8), 988–997. , Scherf, U. P. (2003) Exploration normalization, and summaries of high density oligonucleotide array probe level data.

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