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Bioinformatics has always been driven by the availability of data—sequential, structural, and most recently functional. The availability of sequence data brought into biology a cadre of computer scientists with special skills in string processing. The availability of structural data brought in technical experts in visualization and computational geometry. This most recent development—the availability of relatively large data sets measuring the expression of genes within cells—has helped attract yet another group of scientists—the data miners, machine learners and statisticians. Outline the content of the following chapters so that readers may choose for themselves the path that suits them. Nonetheless, our intent and contention is that the current ordering of the chapters provides the most efficient way of acquiring the content of this book. Introduction. Here we establish the motivation and the scope of this book and touch upon substantial obstacles to success in the successful application of bioinformatics to an integrative genomics. The notion of an interdisciplinary functional genomics pipeline is introduced.The promise and limitations of functional genomics techniques, the nature of various kinds of genomic data, and the central role played by the discipline of bioinformatics are outlined. Chapter 2. Experimental Design. This chapter develops a framework for approaching the design of microarray?driven functional genomics experiments. Very little here is quantitative or mathematical. Chapter 3. Microarray Measurements to Analyses. We lay the foundations for performing analyses of microarray data sets. This is the first of the more quantitative and mathematical chapters. Start with a discussion of the acquisition of digital data from the two most widely employed classes of microarrays. Chapter 4. Genomic Data?Mining Techniques. When gene expression is measured in more than two samples, gene expression patterns have to be analyzed using methods that consider the coordinated interactions of genes across multiple conditions. This chapter assesses the components of biomedical experiments that can be included in a data?mining investigation. Chapter 5. Bio?Ontologies, Data Models, Nomenclature. This chapter addresses possibly the least exciting but the most pressing bioinformatics need for genomic research: creating and using comprehensive annotations of gene function, storing and organizing microarray expression data, and ensuring standardized access to these data. Chapter 6. From Functional Genomics to Clinical Relevance: Getting the Phenotype Right. Here we address the process of translating the functional genomics research agenda into one of clinical relevance. Chapter 7. The Near Future. As the techniques and goals of functional genomics are in rapid flux, we engage in some short?term forecasting to guide readers planning in this time window. Microarray technologies being developed and recently released are previewed. In this context, the problem of comparing expression measurements across generations of microarray measurement platforms is appraised. More broadly, the different kinds of software required for the successful functional genomics enterprise are described. Finally, a model to meet the training needs of this new discipline is outlined.