Back to Search Start Over

MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer

Authors :
Ge Zhang
Yitong Chen
Chaokun Yan
Jianlin Wang
Wenjuan Liang
Junwei Luo
Huimin Luo
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