A hidden Markov tree model for testing multiple hypotheses corresponding to Gene Ontology gene sets
2018
Background
Testing predefined gene categories has become a common practice for scientists analyzing high throughput transcriptome data. A systematic way of testing gene categories leads to testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The relationships among gene categories induce logical restrictions among the corresponding null hypotheses. An existing fully Bayesian method is powerful but computationally demanding.
Keywords:
- False discovery rate
- Expectation–maximization algorithm
- Directed acyclic graph
- Decision tree model
- Null hypothesis
- Bioinformatics
- Biology
- Hidden Markov model
- Ontology
- Artificial intelligence
- Bayesian probability
- Pattern recognition
- Throughput
- Expression quantitative trait loci
- Theoretical computer science
- Genetics
- Posterior probability
- Correction
- Source
- Cite
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