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:

  • Explainable AI
  • Simulations of biological systems
  • Bioinformatics workflows
  • Multi-omics integration techniques, combining different types of measurements.
  • Pathway analysis

We work on various biological problems, including:

  • The impact of genomics alterations in cancer
  • Mechanisms of protein aggregation
  • Finding novel biomarkers for neurodegenerative disease
  • Unravelling plant-microbiome interactions.

Collaborations

As a group, we are always interested in research collaborations both to develop AI technology and tackle complex research questions on living systems, with research institutes as well as companies. Please contact us if you want to discuss opportunities for collaboration.

Embedding

AI Technology for Life is one of the research groups in the AI & Data Science division within the department of Information and Computing sciences.

Background

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:

  1. 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.
  2. 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.