Our groups interest lies in understanding the basis of biological heterogeneity using computational approaches applied to ‘omics data in a disease context. We employ artificial intelligence (AI) and data science (DS) methods, not only to address the underlying causes of this heterogeneity, but also the interactions between different molecular ‘omics layers. We have a strong specialisation in the application area of oncology, but are also very interested in immunology and neurodegenerative diseases.
We believe that by better understanding global and local patterns in biological data, we can better differentiate the mechanistics behind biological and molecular processes from technical noise. As such, we rely on establishing close ties with experimental collaborators who are able to provide us with large scale and high dimensional datasets profiled using state of the art and exotic profiling techniques. We aim to take advantage of recent technological advances that are able to resolve single cell, spatial, temporal and genetic heterogeneity.
The group has a strong core expertise in analysis and interpretation of cancer multiomics data, with exposure to a wide number of omics layers and tumour entities. We are in particular very interested in understanding the heterogeneous patient response to drugs and are interested in accurate classification of tumour entities, patient specific molecular drivers, tumour microenvironment and TILs, tumour evolution and heterogeneity, and germline predisposing factors.
Naveed Ishaque, Ph.D.
Group leader Computational Oncology
*these authors contributed equally
§corresponding author
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