Code and Datasets for the paper "Towards early detection of adverse drug reactions: combining pre-clinical drug structures and post-market safety reports", published on BMC Medical Informatics and Decision Making in 2019.
This repository contains source code for paper “Towards early detection of adverse drugreactions: combining pre-clinical drug structuresand post-market safety reports” (accepted by BMC Medical Informatics and Decision Making).
Please kindly cite the paper if you use the code, datasets or any results in this repo or in the paper:
Liu, R., Zhang, P. Towards early detection of adverse drug reactions: combining pre-clinical
drug structures and post-market safety reports. BMC Med Inform Decis Mak 19, 279 (2019) doi:10.1186/s12911-019-0999-1
In this paper, we propose a label propagation framework to enhance drug safety signals by combining pre-clinical drug chemical structures with post-marketing safety reports from FDA Adverse Event Reporting System (FAERS).
We apply the label propagation framework to four popular signal detection algorithms (PRR, ROR,MGPS, BCPNN) and find that our proposed framework generates more accurate drug safety signals than the corresponding baselines.
Fig. 1: The overall framework for label propagation based signal detection algorithms. It consists of three main steps: computing original drug safety signals from FAERS reports, constructing a drug-drug similarity network from pre-clinical drug structures, and generating enhanced drug safety signals through a label propagation process.
Datasets used in the paper:
python run.py --input SignalScoresSource --method PRR05 --year all --eval_metrics all --split True