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Köp båda 2 för 3179 kr"Here is a comprehensive book that systematically covers both basic and advanced statistical topics in molecular biology, including parametric and nonparametric, and frequentist and Bayesian methods. I am highly impressed by the breadth and depth of the applications. I strongly recommend this book for both statisticians and biologists who need to communicate with each other in this exciting field of research." (Robert C. Elston, PhD., Director, Division of Genetic and Molecular Epidemiology, Case Western Reserve University) "An extraordinary exposition of the central topics of modern molecular biology, presented by practicing experts who weave together rigorous theory with practical techniques and illustrative examples." (George C. Newman, MD, PhD, Chairman, Neurosensory Sciences, Albert Einstein Medical Center) "I cannot think of anything we need now in translation research field more than more efficient cross talk between molecular biology and statistics. This book is just on target. It fills the gap." (Iman Osman, MB, BCh, MD, Director, Interdisciplinary Melanoma Cooperative Program, New York University Langone Medical Center)
Basic Statistics.- Experimental Statistics for Biological Sciences.- Nonparametric Methods for Molecular Biology.- Basics of Bayesian Methods.- The Bayesian t-Test and Beyond.- Designs and Methods for Molecular Biology.- Sample Size and Power Calculation for Molecular Biology Studies.- Designs for Linkage Analysis and Association Studies of Complex Diseases.- to Epigenomics and Epigenome-Wide Analysis.- Exploration, Visualization, and Preprocessing of HighDimensional Data.- Statistical Methods for Microarray Data.- to the Statistical Analysis of Two-Color Microarray Data.- Building Networks with Microarray Data.- Advanced or Specialized Methods for Molecular Biology.- Support Vector Machines for Classification: A Statistical Portrait.- An Overview of Clustering Applied to Molecular Biology.- Hidden Markov Model and Its Applications in Motif Findings.- Dimension Reduction for High-Dimensional Data.- to the Development and Validation of Predictive Biomarker Models from High-Throughput Data Sets.- Multi-gene Expression-based Statistical Approaches to Predicting Patients Clinical Outcomes and Responses.- Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs.- Statistical Methods for Proteomics.- Meta-Analysis for High-Dimensional Data.- Statistical Methods for Integrating Multiple Types of High-Throughput Data.- A Bayesian Hierarchical Model for High-Dimensional Meta-analysis.- Methods for Combining Multiple Genome-Wide Linkage Studies.- Other Practical Information.- Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies.- Stata Companion.