DrugReLink is a tool that optimizes, trains, and evaluates predictive models for links in Hetionet using different network representation learning methods to compare learned features versus the topological features presented in [himmelstein2017].
This package was developed during the master's thesis of Lingling Xu under the supervision of Dr. Charles Tapley Hoyt.
Install from GitHub with:
$ git clone https://github.com/drugrelink/drugrelink.git
$ cd drugrelink
$ pip install -e .Run on a subgraph of Hetionet with:
Download examples of configuration files from /resources/config_examples/
You can specify the output file path by adding "output_directory: path_of_output" to the configuration file.
$ drugrelink path_of_the_config_file| [himmelstein2017] | Himmelstein, D. S., et al. (2017). Systematic integration of biomedical knowledge prioritizes drugs for repurposing. ELife, 6. |