An approximate nonmyopic computation for value of information
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Algorithms for choosing differential gene expression experiments
RECOMB '99 Proceedings of the third annual international conference on Computational molecular biology
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Computational selection of distinct class- and subclass-specific gene expression signatures
Journal of Biomedical Informatics
Revising regulatory networks: from expression data to linear causal models
Journal of Biomedical Informatics
Expected value of experimentation in causal discovery from gene expression studies
Expected value of experimentation in causal discovery from gene expression studies
Active learning for structure in Bayesian networks
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Causal discovery from a mixture of experimental and observational data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Artificial Intelligence in Medicine
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The main topic of this paper is evaluating a system that uses the expected value of experimentation for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knock-out experiment) and observations (e.g., passively observing the expression level of a "wild-type" gene). We introduce a system called GEEVE (causal discovery in Gene Expression data using Expected Value of Experimentation), which implements expected value of experimentation in discovering causal pathways using gene expression data. GEEVE provides the following assistance, which is intended to help biologists in their quest to discover gene-regulation pathways: • Recommending which experiments to perform (with a focus on "knock-out" experiments) using an expected value of experimentation (EVE) method. • Recommending the number of measurements (observational and experimental) to include in the experimental design, again using an EVE method. • Providing a Bayesian analysis that combines prior knowledge with the results of recent microarray experimental results to derive posterior probabilities of gene regulation relationships.In recommending which experiments to perform (and how many times to repeat them) the EVE approach considers the biologist's preferences for which genes to focus the discovery process. Also, since exact EVE calculations are exponential in time, GEEVE incorporates approximation methods. GEEVE is able to combine data from knock-out experiments with data from wild-type experiments to suggest additional experiments to perform and then to analyze the results of those microarray experimental results. It models the possibility that unmeasured (latent) variables may be responsible for some of the statistical associations among the expression levels of the genes under study.To evaluate the GEEVE system, we used a gene expression simulator to generate data from specified models of gene regulation. Using the simulator, we evaluated the GEEVE system using a randomized control study that involved 10 biologists, some of whom used GEEVE and some of whom did not. The results show that biologists who used GEEVE reached correct causal assessments about gene regulation more often than did those biologists who did not use GEEVE. The GEEVE users also reached their assessments in a more cost-effective manner.