Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery
Indexed incrossrefdoajpubmed
Abstract
Precise drug target discovery is pivotal to mitigating the escalating costs and high attrition rates that characterize pharmaceutical research and development. Given that traditional single-omics methods often fail to elucidate the systemic complexity of human diseases, deep learning (DL)-enabled multi-omics integration has emerged as a transformative frontier. This review systematically summarizes the advancements in DL-driven multi-omics integration for drug target discovery. First, the multi-omics data foundation and integration strategies are delineated, followed by an exploration of the DL architectures utilized for processing such data. Subsequently, the efficacy of DL-driven multi-omics integration is…
Citation impact
4
total citations
- FWCI
- 36.53
- Percentile
- 99%
- References
- 141
Too recent for citation history.
Authors
5Topics & keywords
Topics
Keywords
- Context (archaeology)
- Druggability
- Drug discovery
- Data integration
- Identification (biology)
- Transformative learning
- Prioritization
- Deep learning
No related works found for this paper.