By Shoemaker ., et al., Eds
As reviews utilizing microarray know-how have developed, so have the information research tools used to investigate those experiments. The CAMDA convention performs a job during this evolving box by way of offering a discussion board during which traders can study a similar information units utilizing assorted tools. equipment of Microarray information Analysis IV is the fourth publication during this sequence, and specializes in the real factor of associating array info with a survival endpoint. past books during this sequence inquisitive about category (Volume I), trend popularity (Volume II), and quality controls concerns (Volume III). during this quantity, 4 lung melanoma information units are the focal point of research. We spotlight 3 educational papers, together with one to help with a simple knowing of lung melanoma, a evaluate of survival research within the gene expression literature, and a paper on replication. additionally, 14 papers provided on the convention are integrated. This booklet is a wonderful reference for tutorial and commercial researchers who are looking to retain abreast of the state-of-the-art of microarray facts research. Jennifer Shoemaker is a college member within the division of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the melanoma and Leukemia staff B Statistical heart, Duke collage clinical middle. Simon Lin is a college member within the division of Biostatistics and Bioinformatics and the executive of the Duke Bioinformatics Shared source, Duke collage scientific middle.
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Extra info for Methods of Microarray Data Analysis IV
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Bioinformatics 18:1625-32 Nimgaonkar A, Sanoudou D, Butte AJ, Haslett JN, Kunkel LM, Beggs AH, Kohane IS (2003) Reproducibility of gene expression across generations of Affymetrix microarrays. BMC Bioinformatics 4:27 Park PJ, Tian L, Kohane IS (2002) Linking gene expression data with patient survival times using partial least squares. Bioinformatics 18 Suppl 1:S120-7 Perez-Enciso M, Tenenhaus M (2003) Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach.
S. Food and Drug Administration Abstract: Microarray technology provides exciting tools for monitoring expression levels of hundreds or thousands of genes simultaneously. Good microarray studies have clear objectives. To make meaningful statistical interpretation of study results obtained from microarray experiments, the design of the experiments must consider some degree of replication to allow for the description of sources of variations. In this article, we present an overview of replicate designs that incorporate measurement variability to address its study objectives.
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