Abstract

Living organisms are the most complex objects we know of in the universe, with many different molecules, chemical reactions and processes arranged in precise patterns that give rise to cells, tissues, organs and individuals. We can measure many of these molecules, from DNA through RNA to proteins at scale, and we can often place these molecules, cells and tissue structures into 3 dimensiona space, sometimes in living systems with changes over time. The result is an increasingly detailed observation on life, from genome onwards. However, analysing these multi-modal datasets is challenging for a variety of reasons. Over the last decade Machine Learning, Deep Learning and AI - all part of a continuum of high parameter statistical models has been making great strides in predicting and sometimes providing insight into this work.

I will provide a brief overview of the technologies and challenges in this area, outline EMBL’s strategic role in Europe in coordinating the data and insight for these efforts and then provide some exemplars from my own research and other colleagues of using AI to understand life.

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