Multi-omics Prediction from High-content Cellular Imaging with Deep Learning
This is a virtual seminar. For a Zoom link, please see "Venue details". Please consider subscribing to mailing list: web.maillist.ox.ac.uk/ox/subscribe/ai4mch
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics — a deep learning approach that predicts multi-omics in a cell population directly from high-content images stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cell (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictors based on the mean observed training set abundance.
Date: 7 November 2023, 15:00 (Tuesday, 5th week, Michaelmas 2023)
Venue: https://zoom.us/j/91910322449?pwd=eXRTM01Bb1gyYjRBaWpQQmZEV3VqQT09
Speaker: Dr Cuong Nguyen (GSK, San Francisco, CA, USA)
Organising department: Department of Psychiatry
Organiser: Dr Andrey Kormilitzin (University of Oxford)
Organiser contact email address: andrey.kormilitzin@psych.ox.ac.uk
Part of: Artificial Intelligence for Mental Health Seminar Series
Booking required?: Not required
Booking url: https://web.maillist.ox.ac.uk/ox/info/ai4mch
Booking email: andrey.kormilitzin@psych.ox.ac.uk
Audience: Public
Editor: Andrey Kormilitzin