Advances in Statistical Bioinformatics

Models and Integrative Inference for High-Throughput Data

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Science
Cover of the book Advances in Statistical Bioinformatics by , Cambridge University Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9781107241541
Publisher: Cambridge University Press Publication: June 10, 2013
Imprint: Cambridge University Press Language: English
Author:
ISBN: 9781107241541
Publisher: Cambridge University Press
Publication: June 10, 2013
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Cambridge University Press

Cover of the book Dynamics of Particles and Rigid Bodies by
Cover of the book Finite Geometry and Combinatorial Applications by
Cover of the book Flora of Great Britain and Ireland: Volume 4, Campanulaceae - Asteraceae by
Cover of the book Managing Extreme Climate Change Risks through Insurance by
Cover of the book Mechanics of Composite Structures by
Cover of the book The Cambridge Dictionary of Psychology by
Cover of the book Einstein's Opponents by
Cover of the book Physics and Chemistry of Circumstellar Dust Shells by
Cover of the book The Cambridge Companion to Popper by
Cover of the book Slavery, Race, and Conquest in the Tropics by
Cover of the book The Hellenistic West by
Cover of the book Reuse and Renovation in Roman Material Culture by
Cover of the book Inequalities by
Cover of the book Nietzsche's Philosophy of History by
Cover of the book An Anthology of Informal Latin, 200 BC–AD 900 by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy