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Advances in Statistical Bioinformatics - Models and Integrative Inference for High-Throughput Data (Electronic book text)
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Advances in Statistical Bioinformatics - Models and Integrative Inference for High-Throughput Data (Electronic book text)
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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.
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