| Title: | Context-specific drug scoring for precision repurposing via synergizing label propagation with heterogeneous networks |
| Journal: | Journal of Big Data |
| Published: | 19 Aug 2026 |
| DOI: | https://doi.org/10.1186/s40537-026-01541-2 |
| URL: | http://dx.doi.org/10.1186/s40537-026-01541-2 |
Publication 20956
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Abstract
While biological and pharmaceutical knowledge networks have significantly propelled drug repurposing efforts, reliance solely on these networks is insufficient for accurately addressing genetic and phenotypic variance. This limitation highlights the need for an integrative approach that leverages context-specific data to enhance the precision of drug repurposing. We introduce a network-based integrative drug scoring approach that synergistically incorporates data-driven and knowledge-driven networks without requiring their direct integration. We developed a synergistic label propagation algorithm that facilitates information transfer from data-driven to knowledge-driven networks. To enable context-specific drug repurposing, we constructed a data-driven disease-disease association network utilizing European-specific genetic information and a knowledge-driven drug-target protein association network. In a proof-of-concept study, drug scoring was applied to identify candidate drugs for rheumatoid arthritis, asthma, and multiple sclerosis. Compared with a representative direct-integration benchmark, the proposed method achieved an average AUC of 0.701, corresponding to a 9.71% improvement. These results provide preliminary evidence supporting the utility of the proposed framework. A supportive EHR-based medication association analysis using UK Biobank participants' medication records for rheumatoid arthritis highlighted three prioritized drugs or drug classes: Carfilzomib, Etanercept, and Artenimol. Overall, this framework provides a flexible strategy for incorporating context-specific genetic information into network-based drug repurposing.</p>
11 Authors
- Yonghyun Nam
- Sang-Hyuk Jung
- Jakob Woerner
- Patrick J. Lawrence
- Dong-gi Lee
- Shu Yang
- Tianhua Zhai
- Hyunjung Shin
- Xia Ning
- Li Shen
- Dokyoon Kim
1 Application
| Application ID | Title |
|---|---|
| 79011 | Development of artificial intelligence model for dementia precision medicine and diversification of diagnosis |
Enabling scientific discoveries that improve human health