This repository contains scripts and data files for timestamping biomedical evidence available in the Open Targets Platform, and calculating novelty scores for target-disease associations in the Platform.
With the aim of harnessing and expanding the capabilities of the Open Targets Platform, we have developed a method for tracking biomedical evidence over time and using it to discover potential therapeutic targets with emerging novelty signals. We start with a comprehensive effort to retrospectively timestamp millions of pieces of evidence describing relationships between human targets and diseases that Open Targets has collected over the years. We then reconstruct a timeline for each target-disease association in the Open Targets Platform and define a metric to quantify the degree of novelty of a target in the context of a disease based on the current state of knowledge.
The scripts require only a standard computer with enough RAM to support the in-memory operations.
This package is supported for macOS. The package has been tested on the following systems:
- macOS: Sequoia (15.0.1)
numpy # 1.26.4
pandas # 2.1.0
pyspark # 3.3.2
scipy # 1.11.4
- Parse timestamps for Open Targets Platform evidence from original resources:
python timestampsParser.py
- Add timestamps to evidence files in Open Targets Platform:
python parsePublicationYearGC.py
- Generate timeseries data and assess target-disease associations' novelty:
python timeseries.py
To demo timeseries.py use data/demo_evidence as input containing the timestamped evidence for asthma and TSLP association.
To run the scripts for Open Targets Platform 23.06 as a whole we used a Google Cloud machine with the following specifications:
gcloud dataproc clusters create cf-timeseries
--region europe-west1 --zone europe-west1-d
--single-node --master-machine-type n2-highmem-128
--master-boot-disk-size 500 --image-version 2.0-debian10
--project open-targets-eu-dev
Run times are specified in the scripts.
- Plot timeseries for a given target-disease association:
import plotTimeseries
plotTimeseries.plotTargetDisease(targetId="ENSG00000145777", # TSLP
diseaseId="MONDO_0004979", # asthma
showScore=True, showEvidence=True, showNovelty=True,
img="data/demo_timelines.png")) # overlap of the 3 plots shown below
- List novel target-disease associations in the Open Targets Platform in 2023:
python novels.py
- Analyse temporal trends in novel drug targets discovery since 2000.
python noveltyTrends.py
- Analyse the co-ocurrance of novelty peaks across resources of the same type.
python noveltyCorrelation.py
- General analysis of outputs:
analysis.ipynb
This project is covered under the Apache 2.0 License.