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A Bayesian mixture model for differential gene expression
Kim Anh Do
, Peter Müller, Feng Tang
Biostatistics
Research output
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Contribution to journal
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Article
›
peer-review
118
Scopus citations
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Dive into the research topics of 'A Bayesian mixture model for differential gene expression'. Together they form a unique fingerprint.
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Mathematics
Gene Expression
85%
Differential Expression
84%
Bayesian Model
76%
Mixture Model
71%
Empirical Bayes
27%
Probability Model
26%
Gene
22%
Model-based
20%
Nonparametric Bayes
16%
Empirical Bayes Method
16%
Mixture of Normal Distributions
15%
False Discovery Rate
14%
Microarray
14%
Plug-in
14%
Cancer
11%
Null
10%
Verify
9%
Experiment
8%
Evaluation
8%
Simulation Study
8%
Necessary
7%
Simulation
7%
Alternatives
6%
Estimate
5%
Model
3%
Business & Economics
Gene Expression
100%
Mixture Model
86%
Inference
56%
Empirical Bayes
47%
Probability Model
28%
Gene
20%
Microarray
17%
Normal Distribution
12%
Cancer
11%
Simulation Study
10%
Simulation
10%
Evaluation
7%
Experiment
6%
Alternatives
6%