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Providing genome-informed personalized treatment is a goal of
modern medicine. Identifying new translational targets in nucleic
acid characterizations is an important step toward that goal. The
information tsunami produced by such genome-scale investigations is
stimulating parallel developments in statistical methodology and
inference, analytical frameworks, and computational tools. Within
the context of genomic medicine and with a strong focus on cancer
research, this book describes the integration of high-throughput
bioinformatics data from multiple platforms to inform our
understanding of the functional consequences of genomic
alterations. This includes rigorous and scalable methods for
simultaneously handling diverse data types such as gene expression
array, miRNA, copy number, methylation, and next-generation
sequencing data. This material is written for statisticians who are
interested in modeling and analyzing high-throughput data. Chapters
by experts in the field offer a thorough introduction to the
biological and technical principles behind multiplatform
high-throughput experimentation.
The interdisciplinary nature of bioinformatics presents a research
challenge in integrating concepts, methods, software and
multiplatform data. Although there have been rapid developments in
new technology and an inundation of statistical methods for
addressing other types of high-throughput data, such as proteomic
profiles that arise from mass spectrometry experiments. This book
discusses the development and application of Bayesian methods in
the analysis of high-throughput bioinformatics data that arise from
medical, in particular, cancer research, as well as molecular and
structural biology. The Bayesian approach has the advantage that
evidence can be easily and flexibly incorporated into statistical
methods. A basic overview of the biological and technical
principles behind multi-platform high-throughput experimentation is
followed by expert reviews of Bayesian methodology, tools and
software for single group inference, group comparisons,
classification and clustering, motif discovery and regulatory
networks, and Bayesian networks and gene interactions.
Providing genome-informed personalized treatment is a goal of
modern medicine. Identifying new translational targets in nucleic
acid characterizations is an important step toward that goal. The
information tsunami produced by such genome-scale investigations is
stimulating parallel developments in statistical methodology and
inference, analytical frameworks, and computational tools. Within
the context of genomic medicine and with a strong focus on cancer
research, this book describes the integration of high-throughput
bioinformatics data from multiple platforms to inform our
understanding of the functional consequences of genomic
alterations. This includes rigorous and scalable methods for
simultaneously handling diverse data types such as gene expression
array, miRNA, copy number, methylation, and next-generation
sequencing data. This material is written for statisticians who are
interested in modeling and analyzing high-throughput data. Chapters
by experts in the field offer a thorough introduction to the
biological and technical principles behind multiplatform
high-throughput experimentation.
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