This project aims to improve early detection of pancreatic cancer (PC) by exploring its link with diabetes, especially new-onset diabetes (NOD). PC is one of the most difficult cancers to diagnose early, resulting in one of the lowest 5-year relative survival rates at only 12%. Using artificial intelligence (AI) and data from Danish health registries, the project will develop models to predict PC by analysing patient histories, including hospital diagnoses, laboratory measurements, GP and hospital medications, and primary care visits. Early detection of PC is crucial since it significantly increases survival rates. By focusing on patients with NOD, who are at a higher risk of developing PC, the project seeks to identify subtle signs of the disease early on. This will involve creating AI models that can accurately pinpoint individuals at high risk. The project aims to make significant strides in early cancer detection through timely diagnosis and intervention.
Beyond serving as simple intermediary molecules in the transfer of information from DNA to proteins, RNAs must be tightly regulated, and their correct processing is essential for maintaining cellular function. mRNA fate is strongly influenced by a diverse group of covalent modifications and interactions with RNA binding proteins, which regulate all stages of its life cycle. Recent technological advances are generating large quantities of data in this field, yet there is a need for new computational methods to fully utilise these datasets. To this end, we will develop powerful new frameworks to 1) investigate how RNA modifications are dynamically regulated at the single-cell level and 2) identify the molecular determinants underlying modification patterns. In addition, further research is required to elucidate emerging links between RNA modifications and neurological disorders. Our newly developed approaches will be applied in the context of Parkinson’s disease, aiming to pinpoint the regulatory roles and timings of RNA modification in dopaminergic neuron development.
Preserving biodiversity is not only an urgent matter for the natural world, but also for long-term human health. As the world undergoes dramatic human-induced change, we need to identify species most at risk of extinction and formulate mitigation tactics. Current risk identification approaches are very time and resource intensive, making them unfeasible for many species. The genome of an organism provides valuable information about its past, present, and future and could be leveraged to rapidly identify whether a species is at risk. Despite this, we currently lack the tools to do this for a wide range of species. Our research program will bring the world of genomics to species without access to high quality samples or funding by developing new approaches to reliably assemble and analyse their genomes. Furthermore, we will take advantage of recent advances in machine learning to provide a rapid and efficient method to predict extinction risk in living species based on their genomes.