5 papers
Latent Causal Diffusions for Single-Cell Perturbation Modeling
Lars Lorch, Jiaqi Zhang, Charlotte Bunne +3
Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbati…
AI-powered virtual tissues from spatial proteomics for clinical diagnostics and biomedical discovery
Johann Wenckstern, Eeshaan Jain, Yexiang Cheng +9
Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis. Each study use…
Cross-modality Matching and Prediction of Perturbation Responses with Labeled Gromov-Wasserstein Optimal Transport
Jayoung Ryu, Charlotte Bunne, Luca Pinello +2
It is now possible to conduct large scale perturbation screens with complex readout modalities, such as different molecular profiles or high content cell images. While these open t…
3DReact: Geometric deep learning for chemical reactions
Puck van Gerwen, Ksenia R. Briling, Charlotte Bunne +4
Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficie…
Aligned Diffusion Schrödinger Bridges
Vignesh Ram Somnath, Matteo Pariset, Ya-Ping Hsieh +3
Diffusion Schrödinger bridges (DSB) have recently emerged as a powerful framework for recovering stochastic dynamics via their marginal observations at different time points. Desp…