An evaluation of a system that recommends microarray experiments to perform to discover gene-regulation pathways

Artificial Intelligence in Medicine
Changwon Yoo, Gregory F Cooper

Abstract

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 bio...Continue Reading

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Citations

Oct 2, 2007·BMC Bioinformatics·Florian Markowetz, Rainer Spang
May 20, 2016·Applied Informatics·Peter Spirtes, Kun Zhang
Apr 8, 2009·Annals of the New York Academy of Sciences·Changwon Yoo, Erik M Brilz
Feb 6, 2013·European Eating Disorders Review : the Journal of the Eating Disorders Association·Myra J Cooper, Jane Proudfoot
Oct 11, 2005·Drug Discovery Today·Aroosha LaghaeePeter Ghazal
Jul 6, 2014·International Neurourology Journal·Changwon YooJuan Liuzzi

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