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Shortlisted for the 2018 TWS Wildlife Publication Awards in the
edited book category The various species of new world blackbirds,
often intermingled in large foraging flocks and nighttime roosts,
collectively number in the hundreds of millions and are a dominant
component of the natural and agricultural avifauna in North America
today. Because of their abundance, conspicuous flocking behavior,
and feeding habits, these species have often been in conflict with
human endeavors. The pioneering publications on blackbirds were by
F. E. L. Beal in 1900 and A. A. Allen in 1914. These seminal
treatises laid the foundation for more than 1,000 descriptive and
experimental studies on the life histories of blackbirds as well as
their ecology and management in relation to agricultural damage and
other conflicts such as caused by large winter roosting
congregations. The wealth of information generated in over a
century of research is found in disparate outlets that include
government reports, conference proceedings, peer-reviewed journals,
monographs, and books. For the first time, Ecology and Management
of Blackbirds (Icteridae) in North America summarizes and
synthesizes this vast body of information on the biology and life
histories of blackbirds and their conflicts with humans into a
single volume for researchers, wildlife managers, agriculturists,
disease biologists, ornithologists, policy makers, and the public.
The book reviews the life histories of red-winged blackbirds,
yellow-headed blackbirds, common grackles, and brown-headed
cowbirds. It provides in-depth coverage of the functional roles of
blackbirds in natural and agricultural ecosystems. In doing so,
this authoritative reference promotes the development of improved
science-based, integrated management strategies to address
conflicts when resolutions are needed.
As studies using microarray technology have evolved, so have the
data analysis methods used to analyze these experiments. The CAMDA
conference plays a role in this evolving field by providing a forum
in which investors can analyze the same data sets using different
methods. Methods of Microarray Data Analysis IV is the fourth book
in this series, and focuses on the important issue of associating
array data with a survival endpoint. Previous books in this series
focused on classification (Volume I), pattern recognition (Volume
II), and quality control issues (Volume III). In this volume, four
lung cancer data sets are the focus of analysis. We highlight three
tutorial papers, including one to assist with a basic understanding
of lung cancer, a review of survival analysis in the gene
expression literature, and a paper on replication. In addition, 14
papers presented at the conference are included. This book is an
excellent reference for academic and industrial researchers who
want to keep abreast of the state of the art of microarray data
analysis. Jennifer Shoemaker is a faculty member in the Department
of Biostatistics and Bioinformatics and the Director of the
Bioinformatics Unit for the Cancer and Leukemia Group B Statistical
Center, Duke University Medical Center. Simon Lin is a faculty
member in the Department of Biostatistics and Bioinformatics and
the Manager of the Duke Bioinformatics Shared Resource, Duke
University Medical Center.
As microarray technology has matured, data analysis methods have
advanced as well. Methods Of Microarray Data Analysis III is the
third book in this pioneering series dedicated to the existing new
field of microarrays. While initial techniques focused on
classification exercises (volume I of this series), and later on
pattern extraction (volume II of this series), this volume focuses
on data quality issues. Problems such as background noise
determination, analysis of variance, and errors in data handling
are highlighted. Three tutorial papers are presented to assist with
a basic understanding of underlying principles in microarray data
analysis, and twelve new papers are highlighted analyzing the same
CAMDA'02 datasets: the Project Normal data set or the Affymetrix
Latin Square data set. A comparative study of these analytical
methodologies brings to light problems, solutions and new ideas.
This book is an excellent reference for academic and industrial
researchers who want to keep abreast of the state of art of
microarray data analysis.
Microarray technology is a major experimental tool for functional
genomic explorations, and will continue to be a major tool
throughout this decade and beyond. The recent explosion of this
technology threatens to overwhelm the scientific community with
massive quantities of data. Because microarray data analysis is an
emerging field, very few analytical models currently exist. Methods
of Microarray Data Analysis II is the second book in this
pioneering series dedicated to this exciting new field. In a single
reference, readers can learn about the most up-to-date methods,
ranging from data normalization, feature selection, and
discriminative analysis to machine learning techniques. Currently,
there are no standard procedures for the design and analysis of
microarray experiments. Methods of Microarray Data Analysis II
focuses on a single data set, using a different method of analysis
in each chapter. Real examples expose the strengths and weaknesses
of each method for a given situation, aimed at helping readers
choose appropriate protocols and utilize them for their own data
set. In addition, web links are provided to the programs and tools
discussed in several chapters. This book is an excellent reference
not only for academic and industrial researchers, but also for core
bioinformatics/genomics courses in undergraduate and graduate
programs.
Microarray technology is a major experimental tool for functional
genomic explorations, and will continue to be a major tool
throughout this decade and beyond. The recent explosion of this
technology threatens to overwhelm the scientific community with
massive quantities of data. Because microarray data analysis is an
emerging field, very few analytical models currently exist. Methods
of Microarray Data Analysis is one of the first books dedicated to
this exciting new field. In a single reference, readers can learn
about the most up-to-date methods ranging from data normalization,
feature selection and discriminative analysis to machine learning
techniques. Currently, there are no standard procedures for the
design and analysis of microarray experiments. Methods of
Microarray Data Analysis focuses on two well-known data sets, using
a different method of analysis in each chapter. Real examples
expose the strengths and weaknesses of each method for a given
situation, aimed at helping readers choose appropriate protocols
and utilize them for their own data set. In addition, web links are
provided to the programs and tools discussed in several chapters.
This book is an excellent reference not only for academic and
industrial researchers, but also for core bioinformatics/genomics
courses in undergraduate and graduate programs.
As studies using microarray technology have evolved, so have the
data analysis methods used to analyze these experiments. The CAMDA
conference plays a role in this evolving field by providing a forum
in which investors can analyze the same data sets using different
methods. Methods of Microarray Data Analysis IV is the fourth book
in this series, and focuses on the important issue of associating
array data with a survival endpoint. Previous books in this series
focused on classification (Volume I), pattern recognition (Volume
II), and quality control issues (Volume III).
In this volume, four lung cancer data sets are the focus of
analysis. We highlight three tutorial papers, including one to
assist with a basic understanding of lung cancer, a review of
survival analysis in the gene expression literature, and a paper on
replication. In addition, 14 papers presented at the conference are
included. This book is an excellent reference for academic and
industrial researchers who want to keep abreast of the state of the
art of microarray data analysis.
Jennifer Shoemaker is a faculty member in the Department of
Biostatistics and Bioinformatics and the Director of the
Bioinformatics Unit for the Cancer and Leukemia Group B Statistical
Center, Duke University Medical Center. Simon Lin is a faculty
member in the Department of Biostatistics and Bioinformatics and
the Manager of the Duke Bioinformatics Shared Resource, Duke
University Medical Center.
As microarray technology has matured, data analysis methods have
advanced as well. Methods Of Microarray Data Analysis III is the
third book in this pioneering series dedicated to the existing new
field of microarrays. While initial techniques focused on
classification exercises (volume I of this series), and later on
pattern extraction (volume II of this series), this volume focuses
on data quality issues. Problems such as background noise
determination, analysis of variance, and errors in data handling
are highlighted.
Three tutorial papers are presented to assist with a basic
understanding of underlying principles in microarray data analysis,
and twelve new papers are highlighted analyzing the same CAMDA'02
datasets: the Project Normal data set or the Affymetrix Latin
Square data set. A comparative study of these analytical
methodologies brings to light problems, solutions and new ideas.
This book is an excellent reference for academic and industrial
researchers who want to keep abreast of the state of art of
microarray data analysis.
Microarray technology is a major experimental tool for
functional genomic explorations, and will continue to be a major
tool throughout this decade and beyond. The recent explosion of
this technology threatens to overwhelm the scientific community
with massive quantities of data. Because microarray data analysis
is an emerging field, very few analytical models currently exist.
Methods of Microarray Data Analysis II is the second book in this
pioneering series dedicated to this exciting new field. In a single
reference, readers can learn about the most up-to-date methods,
ranging from data normalization, feature selection, and
discriminative analysis to machine learning techniques.
Currently, there are no standard procedures for the design and
analysis of microarray experiments. Methods of Microarray Data
Analysis II focuses on a single data set, using a different method
of analysis in each chapter. Real examples expose the strengths and
weaknesses of each method for a given situation, aimed at helping
readers choose appropriate protocols and utilize them for their own
data set. In addition, web links are provided to the programs and
tools discussed in several chapters. This book is an excellent
reference not only for academic and industrial researchers, but
also for core bioinformatics/genomics courses in undergraduate and
graduate programs.
Microarray technology is a major experimental tool for functional
genomic explorations, and will continue to be a major tool
throughout this decade and beyond. The recent explosion of this
technology threatens to overwhelm the scientific community with
massive quantities of data. Because microarray data analysis is an
emerging field, very few analytical models currently exist. Methods
of Microarray Data Analysis is one of the first books dedicated to
this exciting new field. In a single reference, readers can learn
about the most up-to-date methods ranging from data normalization,
feature selection and discriminative analysis to machine learning
techniques. Currently, there are no standard procedures for the
design and analysis of microarray experiments. Methods of
Microarray Data Analysis focuses on two well-known data sets, using
a different method of analysis in each chapter. Real examples
expose the strengths and weaknesses of each method for a given
situation, aimed at helping readers choose appropriate protocols
and utilize them for their own data set. In addition, web links are
provided to the programs and tools discussed in several chapters.
This book is an excellent reference not only for academic and
industrial researchers, but also for core bioinformatics/genomics
courses in undergraduate and graduate programs.
Readers of this book learn graphic rendering skills quickly with the proven how-to approach that has made Lin the most successful teacher in the field. His method emphasizes speed, confidence, and relaxation, while incorporating many time-saving tricks of the trade.
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