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BackofenLab
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Cherri
https://backofenlab.github.io/Cherri/
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#38
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teresa-m
opened
2 years ago
teresa-m
commented
2 years ago
Important:
Which model should be our core model?
What figure/table should be in the main text or supplement
should we store the models/ datasets somewhere zenodo
should we give a possible use case for Cherri. Where it can be helpful to solve a problem, close a gap?
For later:
parralization of IntaRNA calles in pos/neg data
is there a standard file format for RRIs we can use for Eval mode?
a new method of hutter group to deep learning for tabular data could be used to build a model
build in cross-validation for the model to also report an f1 score
check memory consumption of feature selection + optimization and see if the parallel is an issue here
homologs:
https://github.com/teresa-m/Cherri/issues/32
go away from genome-only sequences with context. e.g. give directly a mRNA
update IntaRNA to genomic version IntaRNA
teresa-m
commented
2 years ago
Possible applications
postprocessing of sRNA, miRNA, gRNA,
off-target predictions
way to pre-filter RRIs for experiments
teresa-m
commented
1 year ago
Tasks:
[ ] get
conda pacakge
running
[ ] Galaxy wrapper?
[ ] update documentaion + paper on how to change IntaRNA paramteres
[ ] Make the training data merge better usable
[ ] Make a more generic input data possible (no need for Chira but it needs a header)
[ ] Get functional testing/ GitHub actions
[ ] Test the train and eval test calls (Maybe someone without prior knowledge)
[ ] Affiliations of @Martin and Egg
[ ] Maybe:train models for single classes e.g. miRNA-mRNA, snoRNA-rRNA
[ ] Maybe: Get LIGR-seq, SPLASH running. Or we take a CLIP-like method as an additional validation set.
[ ] Maybe : Report feature importance
Important:
For later: