Praise for the First Edition
‘…extremely well written…a comprehensive
and up-to-date overview of this important field.’ –
Journal of Environmental Quality
Exploration and Analysis of DNA Microarray and Other
High-Dimensional Data, Second Edition provides comprehensive
coverage of recent advancements in microarray data analysis. A
cutting-edge guide, the Second Edition demonstrates various
methodologies for analyzing data in biomedical research and offers
an overview of the modern techniques used in microarray technology
to study patterns of gene activity.
The new edition answers the need for an efficient outline of all
phases of this revolutionary analytical technique, from
preprocessing to the analysis stage. Utilizing research and
experience from highly-qualified authors in fields of data
analysis, Exploration and Analysis of DNA Microarray and Other
High-Dimensional Data, Second Edition features:
* A new chapter on the interpretation of findings that includes a
discussion of signatures and material on gene set analysis,
including network analysis
* New topics of coverage including ABC clustering, biclustering,
partial least squares, penalized methods, ensemble methods, and
enriched ensemble methods
* Updated exercises to deepen knowledge of the presented material
and provide readers with resources for further study
The book is an ideal reference for scientists in biomedical and
genomics research fields who analyze DNA microarrays and protein
array data, as well as statisticians and bioinformatics
practitioners. Exploration and Analysis of DNA Microarray and
Other High-Dimensional Data, Second Edition is also a useful
text for graduate-level courses on statistics, computational
biology, and bioinformatics.
Over de auteur
DHAMMIKA AMARATUNGA, Ph D, is Senior Director and Janssen
Fellow in the Nonclinical Statistics and Computing Department at
Janssen R&D, a Johnson & Johnson pharmaceutical company.
His research interests include analysis of large multivariate data
sets generated by functional genomics research, robust and
resistant statistical methods, linear and nonlinear modeling, and
biostatistics.
JAVIER CABRERA, Ph D, is Full Professor in the Department
of Statistics at Rutgers University. He has published over 100
articles in his areas of research interest, which include DNA
microarray, data mining of biopharmaceutical databases, computer
vision, statistical computing and graphics, robustness, and
biostatistics. He has also lectured at Cold Spring Harbor
Laboratory, The Hong Kong University of Science and Technology, and
National University of Singapore.
ZIV SHKEDY, Ph D, is Associate Professor and Statistical
Consultant in the Interuniversity Institute for Biostatistics and
Statistical Bioinformatics, Center for Statistics at Hasselt
University, Belgium. He has published numerous journal articles on
the topics of clinical and non-clinical trials, modeling infectious
disease data, dose-response analysis, Bayesian modeling,
bioinformatics, and analysis of gene expression data.
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