Drug repurposing for COVID-19 using literature-based discovery

This repository contains source code related to the publication

Zhang, R., Hristovski, D., Schutte, D., Kastrin, A., Fiszman, M., & Kilicoglu, H. (2021). Drug repurposing for COVID-19 via knowledge graph completion. Journal of Biomedical Informatics, 115, 103696. https://doi.org/10.1016/j.jbi.2021.103696

Prerequisites

• Python 3.6 with packages lxml, numpy, and pandas
• Perl 5 with module Text::NSP
• AWK

Directory Structure

• ./data directory contains input files
• ./preprocessing directory contains scripts for preparing data
• ./filtering directory contains scripts for filtering predications with BERT
• ./models directory contains scripts for knowledge graph completion
• ./predictions directory contains output files from graph completion models

Usage

1. Download and set up SemMedDB
2. Create ./data directory in project's root folder
3. Prepare sub_rel_obj_pyear_edat_pmid_sent_id_sent.tsv.gz file and place it into the ./data/SemMedDB directory
4. Download SemRepped CORD-19 dataset and extract files into ./data/cord-19 directory
5. Prepare SemMedDB and CORD-19 data using the ./preprocessing/run.sh file
6. Run Python notebooks in the ./filtering directory
7. Run Python notebooks in the ./models directory

Contact

Halil Kilicoglu (halil (at) illinois.edu)