ExpressionNet

Jingchun Zhu

November 18, 2006


We developed ExpressionNet, a program that uses Bayesian network learning algorithms to explore relationships among random variables to generate network models that best explains external data.  The software has been used to study the transcriptional response in budding yeast.  A combination of gene expression clusters, genetic information and experimental conditions was used to derive network that models gene expression response to a seriese of environmental triggers.  Detail of the study has been submitted for publication. 

Download Program

Program Documentation

The project is hosted by SourceForge.net Logo .

 

 

The program has been applied to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae, which will soon be published.  The following is the supplemental data for the manuscript.

Supplemental data for the publication “A Bayesian network driven approach to model the transcriptional response to nitric oxide in Saccharomyces cerevisiae

 

Jingchun Zhu, Ashwini Jambhekar, Aaron Sarver and Joseph DeRisi

 

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The supplemental data are organized by the three iterations of modeling.  Network models (files with extension .bn or .matrix) can be viewed using ExpressionNet, a free program developed for Bayesian network learning, which is available at http://expressionnet.sourceforge.net.  Microarray clustering results (files with extension .cdt or .gtr) can be viewed as a pair using JavaTreeView, a free program available at http://jtreeview.sourceforge.net.

 

The datasets include the derived network models, the learning datasets, gene cluster memberships, and complete microarray datasets obtained in this study. 

 

Initial model

1st_model_microarraydata.txt

1st_model_microarraydata.cdt; 1st_model_microarraydata.gtr

 

1st_model_gene_cluster_membership.xls

 

1st_model_node_definitions.xls

 

1st_model_learning_dataset.txt

 

Model: 1st_model_average.matrix and 1st_model.bn

Networks used to construct the model: in the “1st model high-scoring network collection” folder

High-scoring criteria: top 15 percentile using both priors

 

manual_preconstructed.bn

 

 

Second model

2nd_model_microarraydata.txt

2nd_model_microarraydata.cdt; 2nd_model_microarraydata.gtr

 

2nd_model_gene_cluster_membership.xls

 

2nd_model_node_definitions.xls

 

2nd_model_learning_dataset.txt

 

Model: 2nd_model_average.matrix and 2nd_model.bn

Networks used to construct the model: “2nd model high-scoring network collection” folder

High-scoring criteria: top 15 percentile using two both priors

 

 

Third model

3rd_model_microarraydata.txt

3rd_model_microarraydata.cdt; 3rd_model_microarraydata.gtr

 

3rd_model_gene_cluster_membership.xls

 

3rd_model_node_definitions.xls

 

3rd_model_learning_dataset.txt

 

Model: 3rd_model_average.matrix and 3rd_model.bn

Networks used to construct the model: “3rd model high-scoring network collection” folder

High-scoring criteria: the top 25 percentile using both priors

 

Complete microarray data