Structure Learning in Nested Effects Models

  title={Structure Learning in Nested Effects Models},
  author={Achim Tresch and Florian Markowetz},
  journal={Statistical Applications in Genetics and Molecular Biology},
  • A. Tresch, F. Markowetz
  • Published 24 October 2007
  • Computer Science
  • Statistical Applications in Genetics and Molecular Biology
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… 

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