Ava Khamseh (Affiliate)
Biomedical Artificial Intelligence
Section: Biomedical Genomics
Research in a Nutshell
One of the aims of biomedical genomics is to determine causal molecular interactions underlying biological processes. These interactions can be highly complex, involving many dependent variables constituting a given biological system of interest. Quantifying interactions, beyond extracting their magnitude, and moving towards obtaining information on their directionality requires well-designed experiments and/or large-scale individual-level biomedical data.
Currently, our research focuses on causal molecular interactions in the context of cancer & population biomedicine:
1. Cancer biology: Progression of cancer has been widely viewed as following Darwinian evolution, where cells acquiring somatic driver mutations are positively selected, leading to preferential expansions of clones carrying these mutations. Advances in high-throughput DNA- and RNA-sequencing have substantially increased our understanding of late-stage cancer. Yet we still lack a quantitative understanding of the necessary and sufficient early conditions and steps required for the evolution of normal cells into cancer cells.
We design experimental and computational models of early cancer to (i) capture mutational and transcriptional trajectories over time at single-cell resolution, and (ii) quantify how different driver mutation interactions lead to cooperation or competition amongst cells.
2. Population biomedicine: We develop causal machine learning techniques for applications to large-scale biomedical data, such as scRNA-seq and the UK Biobank, whilst avoiding model misspecification. In particular, we are interested in developing and applying (non-)parametric probabilistic ML approaches to (i) extract higher-order interactions from gene expression data, and (ii) quantify causal interactions amongst DNA variants leading to physiological outcomes. This methodologies are based on the framework of Targeted Learning, which provides mathematical guarantees on the final target estimates of interest.
|Dr Ava Khamseh||Group Leader|
|Abel Jansma||PhD student (jointly supervised with Chris Ponting and Luigi Del Debbio)|
|Yuelin Yao||PhD student|
|Hugh Warden||PhD student|
|Olivier Labayle Pabet||PhD student|
|Kelsey Tetley-Campbell||PhD student|
|Richard Kettle||PhD student|
Research Assistant (with Luke Boulter)
|Vidhyah Ganesharaja||MSc student|
|Frank Shuyang Wu||
|Varsha Kesavan||MSc student|
Dr Luke Boulter, University of Edinburgh
Professor Chris Ponting, University of Edinburgh
Professor Martin Taylor, University of of Edinburgh
Dr Catalina Vallejos, University of Edinburgh
Professor Luigi Del Debbio, University of Edinburgh
Dr Sjoerd Beentjes, School of Mathematics, University of Edinburgh
Professor Mark van der Laan, University of California, Berkeley
Understanding cancer initiation and evolution, probabilistic modelling for cancer, non-parametric probabilistic modelling and causal machine learning for large scale biomedical data, Targeted Learning
Single cell biology, cancer models