Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening
Abstract
Optical pooled screening (OPS) combines automated microscopy and genetic perturbations to systematically study gene function in a scalable and cost-effective way. Leveraging the resulting data requires extracting biologically informative representations of cellular perturbation phenotypes from images. We employ a style-transfer approach to learn gene-level feature representations from images of genetically perturbed cells obtained via OPS. Our method outperforms widely used engineered features in clustering gene representations according to gene function, demonstrating its utility for uncovering latent biological relationships. This approach offers a promising alternative to investigate the role of genes in health and disease.
Cite
Text
Bigverdi et al. "Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00790Markdown
[Bigverdi et al. "Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/bigverdi2024cvprw-genelevel/) doi:10.1109/CVPRW63382.2024.00790BibTeX
@inproceedings{bigverdi2024cvprw-genelevel,
title = {{Gene-Level Representation Learning via Interventional Style Transfer in Optical Pooled Screening}},
author = {Bigverdi, Mahtab and Höckendorf, Burkhard and Yao, Heming and Hanslovsky, Phil and Lopez, Romain and Richmond, David},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {7921-7931},
doi = {10.1109/CVPRW63382.2024.00790},
url = {https://mlanthology.org/cvprw/2024/bigverdi2024cvprw-genelevel/}
}