Jonathan Karin
@jonathankarin.bsky.social
3/3 For a tutorial please explore our GitHub repository:
github.com/nitzanlab/An...
github.com/nitzanlab/An...
GitHub - nitzanlab/Annotatability: Annotatability, a method to identify meaningful patterns in single-cell genomics data through annotation-trainability analysis, which estimates annotation congruence...
Annotatability, a method to identify meaningful patterns in single-cell genomics data through annotation-trainability analysis, which estimates annotation congruence using a rich but often overlook...
github.com
December 8, 2024 at 2:05 PM
3/3 For a tutorial please explore our GitHub repository:
github.com/nitzanlab/An...
github.com/nitzanlab/An...
2/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states, infer continuous trajectories from binary labels.
December 8, 2024 at 2:05 PM
2/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states, infer continuous trajectories from binary labels.
For tutorial please explore our GitHub repository:
github.com/nitzanlab/An...
github.com/nitzanlab/An...
GitHub - nitzanlab/Annotatability: Annotatability, a method to identify meaningful patterns in single-cell genomics data through annotation-trainability analysis, which estimates annotation congruence...
Annotatability, a method to identify meaningful patterns in single-cell genomics data through annotation-trainability analysis, which estimates annotation congruence using a rich but often overlook...
github.com
December 5, 2024 at 6:43 PM
For tutorial please explore our GitHub repository:
github.com/nitzanlab/An...
github.com/nitzanlab/An...
2/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states states, infer continuous trajectories from binary labels.
December 5, 2024 at 6:43 PM
2/3 Explore how deep learning training dynamics reveal insights into single-cell and spatial omics data—It can identify erroneous annotations, ambiguous cell states states, infer continuous trajectories from binary labels.