By Frank Emmert-Streib, Matthias Dehmer
This publication is the 1st to target the applying of mathematical networks for interpreting microarray information. this technique is going way past the normal clustering equipment routinely used.
From the contents:
- Understanding and Preprocessing Microarray info
- Clustering of Microarray information
- Reconstruction of the Yeast mobilephone Cycle by means of Partial Correlations of upper Order
- Bilayer Verification set of rules
- Probabilistic Boolean Networks as types for Gene rules
- Estimating Transcriptional Regulatory Networks via a Bayesian community
- Analysis of healing Compound results
- Statistical tools for Inference of Genetic Networks and Regulatory Modules
- Identification of Genetic Networks through Structural Equations
- Predicting useful Modules utilizing Microarray and Protein interplay info
- Integrating effects from Literature Mining and Microarray Experiments to deduce Gene Networks
The publication is for either, scientists utilizing the process in addition to these constructing new research recommendations.
Read Online or Download Analysis of Microarray Data: A Network-Based Approach PDF
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Extra info for Analysis of Microarray Data: A Network-Based Approach
P. 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.
Systematic skew in the data is corrected by lowess normalization. Control targets mixed at known ratios are indicated by color. (a) Left panel: unnormalized data. (b) Right panel: normalized data. j15 16 j 1 Introduction to DNA Microarrays be seen even from single samples that are split in two, separately labeled and hybridized. To alleviate this effect, many methods of normalization have been developed. The most popular is a lowess normalization method utilizing local regression to ﬁt each population in an intensity-dependent fashion .
As also mentioned above, ESTs are assembled to create gene models in silico. Thus, with a collection of gene models, it is possible to design complementary DNA sequences that can be synthetically produced for microarray construction and that can be used to identify the expression of the target gene uniquely. Affymetrix arrays, since they are produced in situ, have always been based on a rational design process. 1 Probe Design The ability of a microarray to report on the identity of a hybridization target depends on the probes attached to the microarray surface.