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

home page By Frank Emmert-Streib, Matthias Dehmer

This ebook is the 1st to target the appliance of mathematical networks for reading microarray facts. this technique is going well past the traditional clustering tools commonly used.

From the contents:

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

The e-book is for either, scientists utilizing the method in addition to these constructing new research options.

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Since many factors unrelated to gene expression can affect the hybridization properties of a probe, each gene is represented not by one probe (like most other types of arrays) but by a population of probes. Summarizing a readout of several probes into a single value for gene expression adds a layer of complexity to data analysis because there are several ways probe sets can be polled and opinions differ on which method is best. Indeed, dozens of methods have been developed [21]. In dealing with Affymetrix arrays, there are two basic steps involved in data analysis.

Cell, 102 (1), 109–126. D. S. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286 (5439), 531–537. , Hudson, J. O. M. (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 403 (6769), 503–511. J. W. (1998) Direct allelic variation scanning of the yeast genome. Science, 281 (5380), 1194–1197. , Metspalu, A. and Remm, M. (2007) Evaluating the performance of commercial whole-genome marker sets for capturing common genetic variation.

P. (1994) Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proceedings of the National Academy of Sciences of the United States of America, 91 (11), 5022–5026. Affymetrix( (2002) Statistical Algorithms description document. Technical Report. , Wu, Z. A. (2006) Comparison of Affymetrix References 22 23 24 25 26 27 GeneChip expression measures. Bioinformatics, 22 (7), 789–794. P. H. (1999) Cheap DNA arrays – it’s not all smoke and mirrors. Nature Biotechnology, 17 (10), 953.

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