Explaining large-scale phosphoproteomics data

The diverse and highly complex nature of modern biological research produces a high volume of data. Within it lie unexpected results, which can reveal new knowledge and/or lead to further testable hypotheses. However, these outcomes might be inherently wrong, resulting from gaps in researchers' prior knowledge, poor choice of statistical analysis, or simply due to experimental error. Thus, there is unprecedented need to develop tools and methodologies to explain and rationalise these results. Specifically, this project aims to develop novel logic programming based algorithms that overcome the limitations of existing analysis tools. Ultimately, these algorithms could assist in patient stratification by explaining disease state differences based on phosphoproteomic data and, in turn, help progress the concept of personalised medicine.