|AI Technology for Life|
In our group we focus on developing AI technology to gain understanding of biological systems from high throughput measurements.
In order to achieve this, we develop and use a wide range of computational technology, including:
We work on various biological problems, including:
Living systems are very complex, with many levels of regulation. Over the past decades, technology has been developed to measure a huge number of data points. For example, several types of ‘omics’ data can be collected on such systems (e.g. genomics, transcriptomics and proteomics). At the same time, AI methodology has improved tremendously, allowing us to make vastly more accurate predictions for many different tasks.
However, data collected from living systems is different from the types of data for which most current AI algorithms have been developed:
Life science data is more challenging to interpret directly: we do not yet understand the detailed functions and mechanisms of the cell and we do not have an intuitive understanding of what a DNA sequence means.
It is usually expensive to collect a lot of data (more than 1000 data points) with good annotations for a prediction task of interest (e.g. disease outcome, phenotype, behaviour). AI algorithms require such annotated data in their training procedure.
These obstacles highlight the need for the clever adaptation of AI algorithms, to make them suitable for learning effectively on life science data.