Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
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
Efficient estimation of the value of information in Monte Carlo models
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
An approximate nonmyopic computation for value of information
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
Journal of Biomedical Informatics
The Journal of Machine Learning Research
Introduction to Causal Inference
The Journal of Machine Learning Research
Computational Statistics & Data Analysis
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The main topic of this paper is modeling the expected value of experimentation (EVE) for discovering causal pathways in gene expression data. By experimentation we mean both interventions (e.g., a gene knockout 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 ''knockout'' experiments) using an expected value of experimentation 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 knockout 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. The results show that the GEEVE system gives better results than two recently published approaches (1) in learning the generating models of gene regulation and (2) in recommending experiments to perform.