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MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer
- Source :
- Frontiers in Pharmacology, Vol 15 (2024)
- Publication Year :
- 2024
- Publisher :
- Frontiers Media S.A., 2024.
-
Abstract
- Synthetic lethality (SL) is widely used to discover the anti-cancer drug targets. However, the identification of SL interactions through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for SL interactions prediction is of great significance. In this study, we propose MPASL, a multi-perspective learning knowledge graph attention network to enhance synthetic lethality prediction. MPASL utilizes knowledge graph hierarchy propagation to explore multi-source neighbor nodes related to genes. The knowledge graph ripple propagation expands gene representations through existing gene SL preference sets. MPASL can learn the gene representations from both gene-entity perspective and entity-entity perspective. Specifically, based on the aggregation method, we learn to obtain gene-oriented entity embeddings. Then, the gene representations are refined by comparing the various layer-wise neighborhood features of entities using the discrepancy contrastive technique. Finally, the learned gene representation is applied in SL prediction. Experimental results demonstrated that MPASL outperforms several state-of-the-art methods. Additionally, case studies have validated the effectiveness of MPASL in identifying SL interactions between genes.
Details
- Language :
- English
- ISSN :
- 16639812
- Volume :
- 15
- Database :
- Directory of Open Access Journals
- Journal :
- Frontiers in Pharmacology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.f1d1d2d57f5e4ca7a570356ae52f6a5c
- Document Type :
- article
- Full Text :
- https://doi.org/10.3389/fphar.2024.1398231