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Published on: October 13, 2023
Empowering the discovery of novel target-disease associations via machine learning approaches in the open targets
Yingnan Han1, Katherine Klinger1, Deepak K Rajpal1
1Translational Sciences, Sanofi US, Framingham, MA, 01701, USA.
Background:
The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications.
Results:
We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein-protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets.
Conclusion:
Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications.
Insights
New machine learning features improve the discovery of novel drug targets for diseases. Integrating tissue specificity, gene ontology, and protein-protein interactions enhances predictions for therapeutic development.
Area of Science:
- Computational biology
- Pharmacogenomics
- Drug discovery
Background:
- The Open Targets (OT) Platform aids in identifying therapeutic drug targets by integrating diverse data on target-disease associations.
- Challenges persist in discovering novel target-disease links due to target pleiotropy and the need for advanced computational integration of biological knowledge.
Purpose of the Study:
- To introduce a novel computational approach for integrating additional target-disease features with machine learning models.
- To enhance the identification of druggable targets for specific disease indications.
Main Methods:
- Derived novel target-disease associations using three data sources: GTEx expression profiles for target tissue specificity, gene ontology for semantic similarity, and protein-protein interaction (PPI) networks for functional interactions.
- Applied machine learning models to evaluate feature importance and predict targets with known drug indications.
Main Results:
- Newly integrated features demonstrated higher importance than existing OT platform features.
- The enhanced features showed superior performance over current association benchmarks.
- The approach may support the discovery of novel therapeutic indications for pursued targets.
Conclusions:
- Generated features represent underlying biological relatedness between targets and diseases.
- The methodology empowers improved prediction of novel indications for drug targets using machine learning.
- This offers a new systematic approach for evaluating drug targets and their potential indications.
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