LeMoNe is a software package for Learning Module Networks from gene expression data.
Michoel, T., Maere, S., Bonnet, E., Joshi, A. M., Saeys, Y., Van den Bulcke, T., Van Leemput, K., et al. (2007). Validating module network learning algorithms using simulated data. BMC BIOINFORMATICS, 8(suppl. 2).
Joshi, A. M., Van de Peer, Y., & Michoel, T. (2008). Analysis of a Gibbs sampler method for model-based clustering of gene expression data. BIOINFORMATICS, 24(2), 176–183.
Joshi, A. M., De Smet, R., Marchal, K., Van de Peer, Y., & Michoel, T. (2009). Module networks revisited: computational assessment and prioritization of model predictions. BIOINFORMATICS, 25(4), 490–496.
Michoel, T., De Smet, R., Joshi, A. M., Van de Peer, Y., & Marchal, K. (2009). Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology, 3(49), 1–13.
Vermeirssen, Vanessa, Joshi, A. M., Michoel, T., Bonnet, E., Casneuf, T., & Van de Peer, Y. (2009). Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development. MOLECULAR BIOSYSTEMS, 5(12), 1817–1830.
Bonnet, E., Tatari, M., Joshi, A. M., Michoel, T., Marchal, K., Berx, G., & Van de Peer, Y. (2010). Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLOS ONE, 5(4).
Bonnet, E., Michoel, T., & Van de Peer, Y. (2010). Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. BIOINFORMATICS, 26(18), i638–i644. Presented at the 9th European Conference on Computational Biology.