antigen specificity prediction
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 265
- Forks
- 50
- Avg merge
- 9d 9h
- Merged PRs (30d)
- 4
Description
Description of feature
The first reasonable methods to predict antigen specificity emerge, e.g.
- ERGO-II (https://www.frontiersin.org/articles/10.3389/fimmu.2021.664514/full, https://github.com/IdoSpringer/ERGO-II)
These are conceptually different from querying databases through sequence distance metrics or autoencoders, as they do not simply model the sequence similarity, but explicitly model the specificity.
Would be nice to call them directly from scirpy.
@FFinotello, potentially another good student task.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing scirpy’s existing analysis entry points and the ERGO-II paper and repository linked in the issue. Determine how antigen-specificity methods should be exposed and which inputs and outputs scirpy can support. The work is done when a defined integration path is implemented and verified for the supported method.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100