Commit fd9f6e75 authored by Ngan Thi Dong's avatar Ngan Thi Dong

update readme

parent a21dd3ce
Simplifying miRNA-disease association prediction
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Simplifying miRNA-disease association prediction
To replecate our result, use the following random seeds: [123, 456, 789, 101, 112]
The code was tested on python 3.7+, all required packages are put in requirements.txt
1. To **generate data for evaluation**, run
`genFoldsData.py --data_dir data/XXX --save_dir data/XXX/folds --randseed RRR`
where XXX is the dataset(either hmdd2 or hmdd3), RRR is the random seed used
2. To evaluate **nimgcn** model, run:
`python eval_nimgcn.py --data_dir data/XXX --fold_dir data/XXX/folds --save_dir data/XXX/results`
other configurable parameters include:
- _sim_type_ should be one of ['functional1', 'functional2', 'gip', 'seq']. The default set up is 'functional2'
- _faulty_ should be either True or False. True means use the faulty calculated similarities
- _save_score_: should be either True or False, corresponding to whether to save to predicted scores or not
- _method_: should be one of ['nimgcn', 'nimgcn1', 'nimgcn2', 'nimgcn3']
- _randseed_: the random seed used to generate train/test split
2. To evaluate **dbmda** model, run:
`python eval_dbmda.py --data_dir data/XXX --fold_dir data/XXX/folds --save_dir data/XXX/results`
other configurable parameters include:
- _sim_type_ should be one of ['functional1', 'functional2', 'gip', 'seq']. The default set up is 'functional2'
- _faulty_ should be either True or False. True means use the faulty calculated similarities
- _save_score_: should be either True or False, corresponding to whether to save to predicted scores or not
- _use_autoencoder_: whether to use autoencoder or not
- _use_seq_sim_: whether to use seq sim or not
- _randseed_: the random seed used to generate train/test split
If you use the code in your work, please cite the following paper:
Dong, Thi Ngan, and Megha Khosla. "A consistent evaluation of miRNA-disease association prediction models." bioRxiv (2020).
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