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Microarray data analysis is a signi cant and broad eld with many unresolved problems This chapter brie y introduces some of the most commonly used methods for the analysis of microarray data, but many topics still remain For example, microarray data can be used to construct gene networks, which are made up of links that represent relationships between genes, such as coregulation Computational models for gene networks include Bayesian networks [61], Boolean networks [62], Petri nets [63], graphical Gaussian models [64], and stochastic process calculi [65] Microarrays can also be studied in conjunction with other topics, such as microarray-related text mining, microarray resources and database construction, drug discovery, drug response study, and design clinical trials Several other types of microarrays are used in addition to gene expression microarrays: protein microarrays (including antibody microarrays), single-nucleotide polymorphism (SNP) microarrays, and chemical compound microarrays.

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Because school psychological records may be used in special education due process hearing or other legal proceedings, practitioners have a responsibility to ensure the kind of detail and quality that would be consistent with reasonable scrutiny in an adjudicative forum (Nagy, 2000) As noted in 1, school psychologists do not seek or store personal information about pupils, parents, teachers, or others that is not needed in the provision of services (EP Principle E, 404; NASP-PPE, III, B, #1) They also are obligated to ensure that records are stored in a manner that protects confidentiality (EP 401; NASP-PPE, IV, D, #5) They take special precautions to ensure that electronically stored information is safe from unauthorized access and that important information is not lost in the event of equipment failure (see Technology in Communication and Recording Keeping later in this chapter).

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Other experimental technologies, such as mass spectrometry, also produce results at a high throughput rate Methods for the analysis of these various types of biological data have a certain degree of similarity with microarray data analysis For example, methods used for the identi cation of differentially expressed genes are similar to the methods used for the identi cation of biomarkers in mass spectrometry data Overall, there are many challenging open topics on analyzing high throughput biological data that can provide research opportunities for the data mining and machine learning community Progress toward solving these challenges and the future directions of research in this area are discussed at various bioinformatics meetings; these include a specialized International Conference for the Critical Assessment of Microarray Data Analysis (CAMDA) that was established in 2000, and that was aimed at the assessment of the state-of-the-art methods in large-scale biological data mining.

If you have programmed in a computer language other than Perl, try reading this book with the help of the online documentation and tutorials. Because this book focuses on a subset of Perl that is most useful for text mining, there are commands and functions that you might want to use but are not discussed here. If you already program in Perl, then peruse the listings in chapters 2 and 3 to see if there is anything that is new to you. These two chapters contain the core Perl knowledge needed for the rest of the book, and once this is learned, the other chapters are understandable. After chapters 2 and 3, each chapter focuses on a topic of text mining. All the later chapters make use of these two chapters, so read or peruse these first. Although each of the later chapters has its own topic, these are the following interconnections. First, chapter 7 relies on chapters 4 and 5. Second, chapter 8 uses the idea of PCA introduced in chapter 7. Third, there are many examples of later chapters referring to the computer programs or output of earlier chapters, but these are listed by section to make them easy to check. The Perl programs in this book are divided into code samples andprogrums. The former are often intermediate results or short pieces of code that are useful later. The latter are typically longer and perform a useful task. These are also boxed instead of ruled. The results of Perl programs are generally called outputs. These are also used for R programs since they are interactive. Finally, I enjoy analyzing text and believe that programming in Perl is a great way to do it. My hope is that this book helps share my enjoyment to both students and researchers.

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