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Thedevelopmentoftechnologiesforhigh–throughputmeasurementofgene expression in biological system is providing powerful new tools for inv- tigating the transcriptome on a genomic scale, and across diverse biol- ical systems and experimental designs. This technological transformation is generating an increasing demand for data analysis in biological inv- tigations of gene expression. This book focuses on data analysis of gene expression microarrays. The goal is to provide guidance to practitioners in deciding which statistical approaches and packages may be indicated for their projects, in choosing among the various options provided by those packages, and in correctly interpreting the results. The book is a collection of chapters written by authors of statistical so- ware for microarray data analysis. Each chapter describes the conceptual and methodological underpinning of data analysis tools as well as their software implementation, and will enable readers to both understand and implement an analysis approach. Methods touch on all aspects of statis- cal analysis of microarrays, from annotation and ?ltering to clustering and classi?cation. All software packages described are free to academic users. The materials presented cover a range of software tools designed for varied audiences. Some chapters describe simple menu-driven software in a user-friendly fashion and are designed to be accessible to microarray data analystswithoutformalquantitativetraining.Mostchaptersaredirectedat microarray data analysts with master’s-level training in computer science, biostatistics, or bioinformatics. A minority of more advanced chapters are intended for doctoral students and researchers.

DNA microarrays. --- Gene expression --- Agrotechnology and Food Sciences. Information and Communication Technology --- Data processing. --- Research --- Methodology. --- Data Processing, Database Management. --- Biomathematics. Biometry. Biostatistics --- Mathematical statistics --- DNA microarrays --- Puces à ADN --- Expression génique --- Methodology --- Data processing --- Recherche --- Méthodologie --- Informatique --- EPUB-LIV-FT SPRINGER-B --- Statistics. --- Human genetics. --- Mathematical statistics. --- Biochemistry. --- Bioinformatics. --- Probabilities. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Probability Theory and Stochastic Processes. --- Biochemistry, general. --- Human Genetics. --- Probability and Statistics in Computer Science. --- Distribution (Probability theory. --- Computer science. --- Statistics . --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Genetics --- Heredity, Human --- Human biology --- Physical anthropology --- Biological chemistry --- Chemical composition of organisms --- Organisms --- Physiological chemistry --- Chemistry --- Medical sciences --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Composition

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The analysis of integrated and co-integrated time series can be considered as the main methodology employed in applied econometrics. This book not only introduces the reader to this topic but enables him to conduct the various unit root tests and co-integration methods on his own by utilizing the free statistical programming environment R. The book encompasses seasonal unit roots, fractional integration, coping with structural breaks, and multivariate time series models. The book is enriched by numerous programming examples to artificial and real data so that it is ideally suited as an accompanying text book to computer lab classes. The second edition adds a discussion of vector auto-regressive, structural vector auto-regressive, and structural vector error-correction models. To analyze the interactions between the investigated variables, further impulse response function and forecast error variance decompositions are introduced as well as forecasting. The author explains how these model types relate to each other.

Statistical science --- Quantitative methods (economics) --- Operational research. Game theory --- stochastische analyse --- time series analysis --- informatietechnologie --- econometrie --- kansrekening --- statistisch onderzoek

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Statistical science --- Pharmacology. Therapy --- Epidemiology --- Forestry --- Artificial intelligence. Robotics. Simulation. Graphics --- neuronale netwerken --- fuzzy logic --- cybernetica --- farmacie --- farmacologie --- toxicologie --- epidemiologie --- KI (kunstmatige intelligentie) --- bossen --- statistisch onderzoek --- AI (artificiële intelligentie)