We are interested in the development of methods for reverse engineering transcription regulatory networks from transcriptome data. Our computational methods are validated on synthetic gene expression data, as well as experimental data for Saccharomyces cerevisiae, Arabidopsis thaliana, Caenorhabditis elegans, and human. We are also working on the integration of other sources of data into our network inference algorithms. Data sources such as protein-protein and protein-DNA interaction data unveil distinct aspects of transcription regulatory networks. Integrating these types of data with gene expression data is a great computational challenge but will undoubtedly allow a more complete insight into the transcription regulatory network.
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