Consensus network inference

 

Andreas Kapourani

Modern high-throughput genomics platforms generate huge amount of biomedical data from different sources, and these data are used to measure diverse, but often related and complementary, information. My goal is to understand the importance of the epigenetic mechanisms (such as DNA methylation and histone modifications) in the regulation of gene expression. The major objective of my PhD project, and one of the major challenges in systems biology, is to infer the topology of gene regulatory networks from high-throughput data alone. Most of the existing methods for GRN inference use only gene expression data; however, our hypothesis is that incorporation of additional information from heterogeneous data sources (e.g. DNA methylation) is vital for improving the inference process.

 

Supervisors: Guido Sanguinetti & Duncan Sproul