Expression data

MQ Zhang. Large-scale gene expression data analysis A new challenge to computational biologists. Genome Res 9 681-688, 1999. 

The ultimate goal of microarray-based expression analysis is to acquire a comprehension of the entire cellular process, in order to exploit and to standardize the multidi-menisional relations between genotype and phenotype. However, an increasingly important parameter, which has not yet been substantially taken into account, is the role of cellular translation. This means that mRNA expression data need to be correlated with the assortment of proteins actually present in the cell. One approach is based on the use of microarrays containing double-stranded DNA probes for the analysis of DNA-protein interaction and, thus, the detection and identification of DNA-binding proteins by means of fluorescence [130] or mass spectrometry analysis [131]. Moreover, substantial efforts are currently under way to develop protein, antibody, or even cell arrays, applicable to the cor-... 

Kalocsai P, Shams S. Visualization and analysis of gene expression data. J Assoc Lab Automation 1999 4 58-61. 

Technology providers use quantitative immunoassays to determine expression data of field material for regulatory submissions. Regulatory authorities require that expression levels of introduced proteins in various plant parts be determined by quantitative, validated methods. Immunoassays are also used to generate product characterization data, to assess food, feed and environmental characteristics, to calculate concentrations for toxicology studies and to obtain tolerance exemption or establish tolerances for pesticidal proteins. 

Tan, Y. X., Shi, L. B Tong, W. D Hwang, G. T. G., Wang, C. (2004). Multiclass tumor classification by discriminant partial least squares using microarray gene expression data and assessment of classification models. Compu. Biol. Chemis. 28(3), 235-244. 

Friedman, N., Linial, M., Nachman, I. and Pe er, D. (2000), Using Bayesian networks to analyze expression data ,/ Comput. Biol, 7, 601-620. 

Shrager, J., Langley, P. and Pohoville, A. (2002), Guiding revision of regulatory models with expression data , Pacific Symposium on Biocomputing, Vol. 7, World Scientific, pp. 486-497. 

Bowtell DDL Options available - from start to finish — for obtaining expression data by microarray. Nature Genet 1999 ... 

Sherlock G Analysis of large-scale gene expression data. Curr Opin Immunol 2000 12 201-205. 

Hilsenbeck SG et al. Statistical analysis of array expression data as applied to the problem of tamoxifen resistance. J Natl Cancer Inst 1999 91 453-459. 

Linton KM, Hey Y, Saunders E, et al. Acquisition of biologically relevant gene expression data by Affymetrix microarray analysis of archival formalin-fixed paraffin-embedded tumours. Br. J. Cancer 2008 98 1398-1402. 

HILSENBECK, S.G., FRIEDRICHS, W.E., SCHIFF, R., O CONNELL, P., HANSEN, R.K., OSBORNE, C.K., FUQUA, S.A., Statistical analysis of array expression data as applied to the problem of tamoxifen resistance, J. Natl. Cancer Inst., 1999,91,453-459. 

ALTER, O., BROWN, P.O., BOTSTEIN, D., Singular value decomposition for genomewide expression data processing and modeling, Proc. Natl. Acad. Sci. USA, 2000, 97, 10101-10106. 

TORONEN, P KOLEHMAINEN, M., WONG, G, CASTREN, E., Analysis of gene expression data using self-organizing maps, FEES Lett., 1999, 451, 142-146. 

VOIT, E.O., RADIVOYEVITHC, T., Biochemical systems analysis of genomewide expression data, Bioinformatics, 2000,16, 1023-1037. 

Computational methods have been applied to determine the connections in systems that are not well-defined by canonical pathways. This is either done by semi-automated and/or curated literature causal modeling [1] or by statistical methods based on large-scale data from expression or proteomic studies (a mostly theoretical approach is given by reference [2] and a more applied approach is in reference [3]). Many methods, including clustering, Bayesian analysis and principal component analysis have been used to find relationships and "fingerprints" in gene expression data [4]. 

However, as with many stories in science, caveolin expression and oncogenic transformation are a complex and controversial tale. Indeed, numerous other types of cancer cells display increased caveolin-1 expression. For example, caveolin-1 expression has been reported to be overexpressed in several cancers including prostate and breast [52], lung [53], and bladder [54], to name a few. What to make of the dizzying array of caveolin expression data in the context of cancer from a diagnostic or therapeutic standpoint at this time is unclear. 

DrugMatrix Iconix Pharmaceuticals, Inc. [http // www.iconixpharma.com/] Pharmacology data integrated with extensive gene expression data... 

That there are many different effects of toxicants aside from changes in gene expression, such as effects on membrane integrity, and these effects might not be extrapolated from single endpoint gene expression data. 

Exploiting Public Databases and Repositories of Gene Expression Data... 

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