02378nam a22003378a 4500 CR9781139226448 UkCbUP 20171019114422.0 m|||||o||d|||||||| cr|||||||||||| 120105s2013||||enk s ||1 0|eng|d 9781139226448 (ebook) 9781107027527 (hardback) UkCbUP UkCbUP rda QH324.2 .A395 2013 572.80285 23 Advances in Statistical Bioinformatics : Models and Integrative Inference for High-Throughput Data / Edited by Kim-Anh Do, Zhaohui Steve Qin, Marina Vannucci. Cambridge : Cambridge University Press, 2013. 1 online resource (514 pages) : digital, PDF file(s). text txt rdacontent computer c rdamedia online resource cr rdacarrier Title from publisher's bibliographic system (viewed on 09 Oct 2015). 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. Biometry Do, Kim-Anh, editor of compilation. Qin, Zhaohui Steve, editor of compilation. Vannucci, Marina, editor of compilation. Print version: 9781107027527 http://dx.doi.org/10.1017/CBO9781139226448 225331 225331