A hidden Markov random field model for genome-wide association studies

DOI10.1093/biostatistics/kxp043zbMath1437.62530OpenAlexW2160736459WikidataQ33566211 ScholiaQ33566211MaRDI QIDQ3303592

John Maris, Hongzhe Li, Zhi Wei

Publication date: 4 August 2020

Published in: Biostatistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1093/biostatistics/kxp043

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (5)

A Markov random field-based approach for joint estimation of differentially expressed genes in mouse transcriptome data ⋮ Bayesian hidden Markov models for dependent large-scale multiple testing ⋮ Hidden Markov model in multiple testing on dependent count data ⋮ A peeling algorithm for multiple testing on a random field ⋮ Statistical identification of important nodes in biological systems

Cites Work

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• Estimation of single-tone signal frequency by using the L-DFT
• The control of the false discovery rate in multiple testing under dependency.
• A hidden spatial-temporal Markov random field model for network-based analysis of time course gene expression data
• Large-Scale Multiple Testing under Dependence
• Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments

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