This repository contains the results described by Hoyt, et al (2019) in [1].
We applied the rational enrichment workflow described in https://github.com/bel-enrichment/bel-enrichment to ten knowledge graphs from the NeuroMMSig inventory.
| [1] | Hoyt, C. T., et al (2019). Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language. Database, Volume 2019, 2019, baz068. |
- rounds: Contains four sub-folders corresponding to the four rounds of rational enrichment that were done in the course of this work. Each round contains several folders named with HGNC gene symbols in which there are three files: one with the INDRA statements as a Python pickle file, one with the original curation sheet generated, and one with the annotations from the curator.
- hbp_enrichment.py: Uses the bel_enrichment package
to generate new curation sheets and make intermediate summary sheets. Requires installation of all
Python packages listed in
setup.cfg. - notebooks: Contains the notebooks used to generate the statistics and figures appearing in the manuscript
- data: Contains intermediate results generated by the notebooks.
This repository can be installed from GitHub with the following command:
$ pip install git+https://github.com/bel-enrichment/results.gitFor developers, can be installed in development mode using the following commands:
$ git clone https://github.com/bel-enrichment/results.git
$ cd results
$ pip install -e .It can be imported and used with the following. Note that it installs at a different name than the GitHub repository.
>>> from hbp_enrichment import repository
>>> graph = repository.get_graph()
>>> graph.summarize()- BEL scripts in this repository are licensed under the CC BY 4.0 license.
- Python source code in this repository is licensed under the MIT license.
This work was done during the Human Brain Pharmacome project <https://pharmacome.scai.fraunhofer.de> funded by the Fraunhofer Society's MAVO program.