Advances in Statistical Bioinformatics

Advances in Statistical Bioinformatics

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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.... on the gene expression levels can lead to both false positives and false negatives for associations in the gene network graph. ... 15.3.1 Estimation Based on (1 Penalization Several methods have been developed to estimate the precision matrix 52 : E71 in Model (15.8). ... Some Statistical Problems and Solutions 319.

Title:Advances in Statistical Bioinformatics
Author:Kim-Anh Do, Zhaohui Steve Qin, Marina Vannucci
Publisher:Cambridge University Press - 2013-06-10


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