This is a re-implementation of the CADA method for phenotype-similarity prioritization.
- Free software: MIT license
- Documentation: https://cada-prio.readthedocs.io/en/latest/
- Discussion Forum: https://github.com/bihealth/cada-prio/discussions
- Bug Reports: https://github.com/bihealth/cada-prio/issues
Install with tune feature enabled:
pip install cada-prio[tune]
Run tuning, e.g., on the "classic" model. Thanks to optuna, you can run this in parallel as long as the database is shared. Each run will use 4 CPUs in the example below and perform 1 trial.
cada-prio tune run-optuna \
sqlite:///local_data/cada-tune.sqlite \
--path-hgnc-json data/classic/hgnc_complete_set.json \
--path-hpo-genes-to-phenotype data/classic/genes_to_phenotype.all_source_all_freqs_etc.txt \
--path-hpo-obo data/classic/hp.obo \
--path-clinvar-phenotype-links data/classic/cases_train.jsonl \
--path-validation-links data/classic/cases_validate.jsonl \
--n-trials 1 \
--cpus=4
# export GITHUB_OWNER=bihealth
# export GITHUB_TOKEN=ghp_<thetoken>
# cd utils/terraform
# terraform init
# terraform import github_repository.cada-prio cada-prio
# terraform validate
# terraform fmt
# terraform plan
# terraform apply
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