Users are excited about the Vermeer model's scalability and flexibility for generative modeling of fluorescent microscopy data and express gratitude to collaborators for their contributions.
We evaluate our model by comparing generated images to true held-out data. We test Vermeer across different axes of generalization, including unseen protein/cell-line combinations, unseen cell-lines, and unseen proteins (shown here). (2/n)
Huge thanks to all of my co-authors Eric Zimmermann, Emre Hayir, @KevinKaichuang for all their contributions! Especially grateful to Fei and @alexijielu for being great mentors. (5/n)
To explain this generalization, we show that Vermeer attends to known localization subsequences, such as the Nuclear Localization Signal which mediates protein translocation into the nucleus. (3/n)
We see Vermeer as a highly scalable and flexible approach for generative modeling of fluorescent microscopy data. We're excited to continue developing and applying Vermeer to uncover new biology. Check out the preprint for more details, and our code will be released on Github soon! (4/n)
@broadinstitute @harvardmed @Harvard #AI4Science #MachineLearning #BioImaging