In our lab we are developing and applying novel advanced statistical methods, drawn from diverse areas such as graph theory, network physics, machine learning and signal processing, to help tackle some of the outstanding challenges in cancer genomics and epigenomics.
We are particularly interested in using the language of networks and statistical mechanics to help elucidate the systems biology principles underlying normal cellular differentiation and how this process is altered in complex diseases such as cancer. We are developing a framework based on statistical mechanics and network theory, in order to help identify the key signaling pathways and nodes in differentiation, cancer and drug resistance.
Another application of networks and graph theory is to the integrative analysis of large-scale multi-dimensional omic data sets. For instance, we have recently used interactome and regulatory networks to integrate epigenetic and gene expression data together, to identify functional epigenetic drivers in cancer and ageing. We are continuing to explore and develop such "Systems Epigenomics" approaches.
We are also involved in numerous more data-driven projects, in close collaboration with clinicians and biologists. One key area of interest is in cancer risk prediction and early detection: we are involved in the analysis of DNA methylation data from large prospective studies in order to assess if DNA methylation marks measured years in advance of disease diagnosis can predict the risk of disease. Another area of interest is in refining the molecular taxonomy of cancers, using perturbation molecular signature approaches to identify novel prognostic and predictive cancer subtypes. ---