The application of A.I in biology and medicine is becoming ubiquitous. The near superhuman accuracies achieved by A.I algorithms can be attributed to A.I’s ability to find patterns in multi-dimensional
(multi-factorial) datasets - generally a very challenging and labour-intensive task for humans. Hence, it is imperative for a field such as biology and medicine, where the output of A.I algorithms affect the health and well-being of individuals, that results are explainable and reproducible. Our group is developing new mathematical methods to help understand the results of AI-based algorithms in the context of Biology. We are a mixed group of Wet-lab (Biologists) and Dry-lab (Mathematicians), working together on understanding and remedying diseases through explainable A.I.