6533b85cfe1ef96bd12bcbf8
RESEARCH PRODUCT
Structure Learning in Nested Effects Models
Florian MarkowetzAchim Treschsubject
Statistics and ProbabilityTraverseComputer scienceMolecular Networks (q-bio.MN)Genes MHC Class IIPerturbation (astronomy)Genes InsectFeature selectionQuantitative Biology - Quantitative Methods03 medical and health sciences0302 clinical medicineGeneticsAnimalsheterocyclic compoundsQuantitative Biology - Molecular NetworksGraphical modelMolecular BiologyQuantitative Methods (q-bio.QM)Oligonucleotide Array Sequence Analysis030304 developmental biologyLikelihood Functions0303 health sciencesNanoelectromechanical systemsModels StatisticalModels GeneticGene Expression ProfilingGenomicsComputational MathematicsDrosophila melanogasterPhenotypeFOS: Biological sciencesBinary dataIdentifiabilityRNA InterferenceLikelihood functionAlgorithmAlgorithms030217 neurology & neurosurgerydescription
Nested Effects Models (NEMs) are a class of graphical models introduced to analyze the results of gene perturbation screens. NEMs explore noisy subset relations between the high-dimensional outputs of phenotyping studies, e.g., the effects showing in gene expression profiles or as morphological features of the perturbed cell. In this paper we expand the statistical basis of NEMs in four directions. First, we derive a new formula for the likelihood function of a NEM, which generalizes previous results for binary data. Second, we prove model identifiability under mild assumptions. Third, we show that the new formulation of the likelihood allows efficiency in traversing model space. Fourth, we incorporate prior knowledge and an automated variable selection criterion to decrease the influence of noise in the data.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-10-24 |