NetwPartLearn is a simulation tool for reverse engineering of genetic networks in case not all gene expression levels are known before transition:
|??00||1100||Experiment 1: M transition vectors|
|?0?0||0100||Experiment 2: M transition vectors|
|0??0||1001||Experiment 3: M transition vectors|
? denotes a gene for which the expression level is unknown at time t. In different experiments the expression levels of different combinations of genes are known.
Genetic networks are modelled as dynamic Bayesian networks (DBNs) with Boolean conditional probability tables (CPTs), i.e. the CPTs can be simplified to Boolean rules. However, the inferred network is a "real" DBN as the CPTs can not be simplified to Boolean rules. NetwPartLearn evaluates in how far the genetic network can be learned from incomplete data by calculating the sensitivity, the positive predictive value and the fidelity for each number of parents k by averaging over a sample with B networks. NetwPartLearn clarifies how many transition vectors M are needed in order to infer a reliable network. It uses a partial learning (PartLearn) strategy to learn the topology and the expectation maximization implementation of LibB to infer the parameters.
This package requires the GNU Scientific Library (GSL), the Perl Compatible Regular Expression library (PCRE) and LibB: GSL, PCRE, LibB
If you use NetwPartLearn in your work please cite:
Kristin Missal, Michael A. Cross and Dirk Drasdo: Gene Network Inference from Incomplete Expression Data: Transcriptional Control of Haemopoietic commitment. (2005), Bioinformatics Advance Access, bti820.
This work was partly supported by the Interdisciplinary Center for Clinical Research, University of Leipzig (Project N02) and the grant BIZ-6 1/1 from the DFG.