http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Meta-analysis and combining information in genetics and genomics
With contributions from leading experts, "Meta-analysis and Combining Information in Genetics and Genomics" provides theory, methods, and guiding principles for meta-analysis and combining information for various types of genetic studies.
A brief introduction to meta-analysis, genetics, and genomics -- Combining information across genome-wide linkage scans -- Genome search meta-analysis (GSMA): a nonparametric method for meta-analysis of genome-wide linkage studies -- Heterogeneity in meta-analysis of quantitative trait linkage studies -- An empirical Bayesian framework for QTL genome-wide scans -- Composite hypothesis testing: an approach built on intersection-union tests and Bayesian posterior probabilities -- Frequentist and Bayesian error pooling methods for enhancing statistical power in small sample microarray data analysis -- Significance testing for small microarray experiments -- Comparison of meta-analysis to combined analysis of a replicated microarray study -- Alternative probe set definitions for combining microarray data across studies using different versions of Affymetrix oligonucleotide arrays -- Gene ontology-based meta-analysis of genome-scale experiments -- Combining genomic data in human studies -- An overview of statistical approaches for expression trait loci mapping -- Incorporating GO annotation information in expression trait loci mapping -- A misclassification model for inferring transcriptional regulatory networks -- Data integration for the study of protein interactions -- Gene trees, species trees, and species networks.
edited by Rudy Guerra, Darlene R. Goldstein.
Description based on opening page (viewed Jan 30, 2018).
Chapman and Hall/CRC mathematical & computational biology series