http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Analysis of Microarray Gene Expression Data
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
DNA, RNA, Protein, and Gene Expression -- Microarray Technology -- Inherent Variability in Microarray Data -- Background Noise -- Transformation and Normalization -- Missing Values in Microarray Data -- Saturated Intensity Readings in Microarray Data -- Experimental Design -- Anova Models for Michrorray Data -- Multiple Testing in Microarray Studies -- Permutation Tests in Microarray Data -- Bayesian Methods for Microarray Data -- Power and Sample Size Considerations at the Planning Stage -- Cluster Analysis -- Principal Components and Singular Value Decomposition -- Self-organizing Maps -- Discrimination and Classification -- Artificial Neural Networks -- Support Vector Machines.