https://github.com/gersteinlab/idash19he
https://github.com/gersteinlab/idash19he
Last synced: 9 days ago
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- Host: GitHub
- URL: https://github.com/gersteinlab/idash19he
- Owner: gersteinlab
- Created: 2020-06-04T12:09:14.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-09-30T17:28:53.000Z (over 3 years ago)
- Last Synced: 2025-04-03T04:41:27.856Z (about 2 months ago)
- Language: C++
- Size: 7.53 MB
- Stars: 7
- Watchers: 13
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README
Awesome Lists containing this project
README
doi:10.5281/zenodo.5542001
For training, go to bob/train folder and do
make clean-all
make all
make deploy
Above will create the training data when the tag SNPs are 10k bp apart from each other (using sorted_tag_SNPs_10k_genotypes.data) for 500 target SNPs in chromosome 1 (sorted_target_SNP_genotypes.data)
Then go to the main folder
To compile:
make compile
To run:
make run ID=id_of_database QUERY=name_of_the_query_file
e.g.:
make run ID=1 QUERY=query_tag_SNPs_1_genotypes.data [for this, the training needs to be done sorted_tag_SNPs_10k_genotypes.data, whihc are tag SNPs 1k apart from each other]
make run ID=10 QUERY=query_tag_SNPs_10_genotypes.data
The outputs are:
- ypredID.data has the probabilities
- targetID.data has the predicted values
- timeID.data has the execution time (round trip, encryption, computation, decryption)
If you prefer to use the Docker file, please create a folder called idash and upload everything but Dockerfile and run_idash.sh in that folder.