Statistical methods in molecular biology

While there is a wide selection of 'by experts, for experts’ books in statistics and molecular biology, there is a distinct need for a book that presents the basic principles of proper statistical analyses and progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology. Statistical Methods in Molecular Biology strives to fill that gap by covering basic and intermediate statistics that are useful for classical molecular biology settings and advanced statistical techniques that can be used to help solve problems commonly encounteredin modern molecular biology studies, such as supervised and unsupervised learning, hidden Markov models, methods for manipulation and analysis of high-throughput microarray and proteomic data, and methods for the synthesis of the available evidences. This detailed volume offers molecular biologists a book in aprogressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of interest to molecular biologists, and the state-of-the-art statistical approaches to analyzing the data. As a volume in the highly successful Methods in Molecular Biology™ series, this work provides the kind of meticulous descriptions and implementation advice for diverse topics that are crucial for getting optimal results.Comprehensive but convenient, Statistical Methods in Molecular Biology will aid students, scientists, and researchers along the pathway from beginning strategies to a deeper understanding of these vital systems of data analysis and interpretation within one concise volume. An easily accessible reference for statistical methods in molecular miology, written by leading researchers in the field Presents a comprehensive guide to self-learning analysis tools for data generated in molecular biology studies, from basic methods to advanced, specialized methods in a progressive style Details the processing, description/visualization, and analyses of the data and software implementation Covers a wide range of statistical methods for the analyses of various types of data collected in different fields of biological sciences,including standard experimental data and high-dimensional data INDICE: Experimental Statistics for Biological Sciences.- Nonparametric Methods in Molecular Biology.- Basics of Bayesian Methods.- The Bayesian t-Test and Beyond.- Sample Size and Power Calculation for Molecular Biology Studies.-Designs for Linkage Analysis and Association Studies of Complex Diseases.- Introduction to Epigenomics and Epigenome-Wide Analysis.- Exploration, Visualization, and Preprocessing of High Dimensional Data.- Introduction to the Statistical Analysis of Two-Color Microarray Data.- Building Networks with MicroarrayData.- 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.- Introduction to the Development and Validation of Predictive Biomarker Models from High-Throughput Datasets.- 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.- 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.- Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies.- Stata Companion.

• ISBN: 978-1-60761-578-1
• Editorial: Humana
• Encuadernacion: Cartoné
• Páginas: 538
• Fecha Publicación: 01/02/2010
• Nº Volúmenes: 1
• Idioma: Inglés