*NetwPartLearn*

### Description

*NetwPartLearn* is a simulation tool for reverse engineering of genetic networks in
case not all gene expression levels are known before transition:

t | t+1 | |

??00 | 1100 | Experiment 1: M transition vectors |

??01 | 0100 | |

??10 | 1110 | |

??11 | 1001 | |

... | ... | |

?0?0 | 0100 | Experiment 2: M transition vectors |

?0?1 | 0110 | |

?1?0 | 0001 | |

?1?1 | 1000 | |

... | ... | |

0??0 | 1001 | Experiment 3: M transition vectors |

0??1 | 1100 | |

1??0 | 1010 | |

1??1 | 0100 | |

... | ... | |

? 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.

### Download and Documentation

Download *NetwPartLearn-0.0.1.tar.gz (source code)*
This package requires the GNU Scientific Library (GSL), the Perl Compatible
Regular Expression library (PCRE) and LibB:
GSL,
PCRE,
LibB

Usage: README

Installation: INSTALL

### Authors

Kristin Missal (code, algorithms)

Dirk Drasdo (algorithms)

Michael A. Cross (biological background)
### Questions and Comments

For any questions and comments to the software, please send your email to
Kristin Missal (kristin@bioinf.uni-leipzig.de).
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.