Microarray Analysis by Mark Schena (Wiley) hailed as the next revolution
in molecular biology, enables scientists to examine the expression levels of
thousands of genes at the same time. Spawning a multibillion-dollar commercial
enterprise, this burgeoning technology impacts every field of biomedicine,
agriculture and the basic and clinical sciences. This high-tech blend of
biology and technology is unraveling the mysteries of the human genome and
expediting the development of safer drugs and new genetic tests. An
essential resource for anyone involved in the life sciences,
Microarray Analysis by Mark Schena (Wiley) is the first textbook
treatment of this innovative technique ever published.
Written by a world-renowned expert Mark Schena, Microarray Analysis contains coverage of biochemical principles, theory, science, and applications of microarray technology. Featuring a rigorous, yet reader-friendly, critique of the diverse ways genome data can be utilized in academic and industrial research, this authoritative text enables investigators in any discipline to gain a working knowledge of possible applications. Featuring discussions of bioinformatics, novel arrays, and applications for clinical studies, as well as a separate chapter on the business of this technology, this well-organized text includes margin definitions, chapter-end questions, and tables, diagrams, photos, and illustrations that facilitate learning. Microarray Analysis concludes with the author's perspective on the future of microarray analysis and its applications.
Microarray Analysis contains an abundance of never-before published material, Including:
-Biochemistry and genomics in microarray analysis
-Target and probe preparation
-Microarray manufacture and detection
-Data analysis and modeling
-Gene expression profiling
-Genetic screening and diagnostics
Microarray Analysis provides any professional or student in the biomedical sciences with the skills necessary to utilize this essential technology.
Mark Schena, Ph.D., a world-recognized pioneer in developing methods, applications, and enabling technologies for the microarray field, is currently a consultant for life science companies and a Visiting Scholar at TeleChem/arrayit.com. The author of more than 30 scientific papers, Dr. Schena co-authored the seminal paper on microarrays published in Science magazine in 1995. Since that year, Dr. Schena has given more than 80 lectures in 15 different countries. Featured as one of NOVA's "Stars of Genomics," he co-founded arrayit.com, an internet-based microarray company, and is Chairman of the Board of NGS-ArrayIt. Inc. Dr. Schena earned his Ph.D. from the University of California, San Francisco, and performed his postdoctoral research at Stanford University.
Methods of Microarray Data Analysis: Papers from CAMDA '00 edited by Simon M. Lin and Kimberly F. Johnson (Kluwer Academic). Contains papers from the 2000 conference on the Critical Assessment of Microarray Data Analysis (CAMDA), held in December at Duke University, Durham, NC. Contents: Contributors. Acknowledgments. Preface. Introduction. Reviews and Tutorials. Data Mining and Machine Learning Methods for Microarray Analysis; W. Dubitzky, et al. Evolutionary Computation in Microarray Data Analysis; J.H. Moore, J.S. Parker. Best Presentation -- CAMDA '00. Using Non-Parametric Methods in the Context of Multiple Testing to Determine Differentially Expressed Genes; G. Grant, et al. Quality Analysis and Data Normalization of Spotted Arrays. Iterative Linear Regresssion By Sector; D.B. Finkelstein, et al. Feature Selection, Dimension Reduction, and Discriminative Analysis. A Method to Improve Detection of Disease Using Selectively Expressed Genes in Microarray Data; V. Aris, M. Recce. Computational Analysis of Leukemia Microarray Expression Data Using the GA/KNN Method; Leping Li, et al. Classical Statistical Approaches to Molecular Classification of Cancer from Gene Expression Profiling; Jun Lu, et al. Classification of Acute Leukemia Based on DNA Microarray Gene Expressions Using Partial Least Squares; Danh V. Nguyen, D.M. Rocke. Applying Classification Separability Analysis to Croarray Data; Zhen Zhang, et al. How many Genes are needed for a Discriminant Microarray Data Analysis; Wentian Li, Yaning Yang. Machine Learning Techniques. Comparing Symbolic and Subsymbolic Machine Learning Approaches to Classification of Cancer and Gene Identification; W. Dubitzky, et al. Applying Machine Learning Techniques to Analysis of Gene Expression Data: Cancer Diagnosis; Kyu-Baek Hwang, et al. Glossary. Index.
Statistical Analysis of Gene Expression Microarray Data by Terry Speed (CRC Press) Preprocessing Issues. Experimental Design and Related Analysis Issues. Classification. Clustering. (Review pending)
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