Digital pathology and tumour microenvironment
Tumour microenvironment plays a critical role in cancer progression and resistance. Advanced imaging approaches allow capturing spatial organisation of different cell types in the tumour microenvironment and their potential interactions. For example, in the image shown, various immune cell types (red, green, magenta, cyan) and cancer cells (blue) show distinct organisation patterns. A major focus of the group is developing explainable AI and bioinformatics approaches to discover predictive patterns from tissue image data and interrogate associated mechanisms
Multi-scale gene function
Despite the tremendous advances in genomics and genetic approaches, a comprehensive understanding of gene functions is still lacking. We employ perturbation and integrative approaches, such as CRISPR and siRNA screens, for studying gene functions. We are interested in understanding how functions at the molecular level propagate to cellular, tissue and organ levels. The activity of proteins is highly dependent on cell context. Therefore, it is crucial to incorporate cell microenvironment and morphology through the use of multi-modal imaging techniques. This requires developing innovative quantitative imaging and analysis methods for modelling biological behaviour at cellular, tissue and organ levels as well as integration of heterogeneous datasets.
Knowledge-driven machine learning
The only way we can harness the power of big biomedical data is through the development of intelligent systems that systematically incorporate existing biological knowledge. Such systems can guide machine learning approaches to discover new patterns in such rich datasets. We demonstrated that such an approach can facilitate the discovery of context-dependent gene functions when applied to large scale genetic screens. Compared to previous methods, knowledge-driven machine learning enabled the extraction of far more information from various datasets.
Data visualisation is an integral part of any scientific approach. We develop bespoke visualisation methods that facilitate pattern and knowledge discovery from large biomedical data. Recent projects are focused on supporting clinical decision making and the use of data visualisation in explaining AI models.