Modern biology increasingly uses the generation and interpretation of large datasets to understand the complex processes that underpin life, health and disease. AI gives us tools and techniques to do this.
AI reveals complex patterns in large datasets that we can use to understand biological processes, predict the onset of disease or engineer novel biological systems. Effectively developing, deploying and utilising AI for the life sciences is an inherently interdisciplinary task, bringing together physicists, chemists, mathematicians, clinicians, and computer scientists. This synergy is crucial for tackling some of biology’s most pressing challenges, from unravelling the complexities of cellular behaviour to forecasting ecological changes and advancing personalised medicine.
Our ability to turn observations into predictions is at the core of quantitative biology. By leveraging mathematical, statistical, and computational techniques, we can transcend what we observe, enabling us to anticipate behaviours and patterns that have yet to be discovered.
Modern experimental methods are allowing us to observe biological systems in ever more detail. The data that these experiments produce is often complex and so requires new mathematical, statistical and machine learning methods to make the most of it. Combining the latest experimental and computational tools we are understanding life as never before and using that understanding to improve health and wellbeing for all.
