Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on learning with Bayesian networks
Learning in graphical models
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Principled computational methods for the validation and discovery of genetic regulatory networks
Principled computational methods for the validation and discovery of genetic regulatory networks
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Researchers often have several different hypothesises on the possible structures of the gene regulatory network (GRN) underlying the biological model they study. It would be very helpful to be able to rank the hypothesises using existing data. Microarray technologies enable us to monitor the expression levels of tens of thousands of genes simultaneously. Given the expression levels of almost all of the well-substantiated genes in an organism under many experimental conditions, it is possible to evaluate the hypothetical gene regulatory networks with statistical methods. We present RankGRN, a web-based tool for ranking hypothetical gene regulatory networks. RankGRN scores the gene regulatory network models against microarray data using Bayesian Network methods. The score reflects how well a gene network model explains the microarray data. A posterior probability is calculated for each network based on the scores. The networks are then ranked by their posterior probabilities. RankGRN is available online at [http://GeneNet.org/bn]. RankGRN is a useful tool for evaluating the hypothetical gene network models’ capability of explaining the observational gene expression data (i.e. the microarray data). Users can select the gene network model that best explains the microarray data.