Statistical Methods in Molecular Biology

Softcover Nachdruck of the original 1. Auflage 2010
ISBN: 978-1-4939-6124-5
Verlag: Humana

Buch, Englisch, 636 Seiten, Previously published in hardcover, Format (B × H): 178 mm x 254 mm, Gewicht: 1206 g

Reihe: Methods in Molecular Biology

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 Includes supplementary material: sn.pub/extras

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Bang, Heejung

Zhou, Xi Kathy

van Epps, Heather L.

Mazumdar, Madhu

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Naturwissenschaften Biowissenschaften Angewandte Biologie Biomathematik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Biochemie (nichtmedizinisch)

Inhaltsverzeichnis

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 High–Dimensional 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.