DSESL: A Deep Stacking Ensemble Model for Synthetic Lethality Prediction
Zhuang Li, Xiaowen Wang, Yulong Li, Hongming Zhu, Qin Liu · 2024
Synthetic lethality (SL) refers to the phenomenon that simultaneous mutation of two genes is lethal to cells, while mutation of either gene alone is not lethal. Exploiting this genetic interaction holds immense clinical potential for selectively killing cancer cells without harming normal cells. Given the vast genomic combinatorial space, relying solely on wet lab experiments for screening synthetic lethal gene pairs is impractical, leading to the emergence of various computational methods. Existing computational methods often rely on single-feature extraction methods or single data sources for prediction, resulting in poor predictive accuracy when faced with unseen genes. In this work, we propose a novel deep ensemble model for synthetic lethality prediction based on a stacking strategy (DSESL). Firstly, leveraging the publicly available SynLethKG knowledge graph, we learn gene embed dings at three different focus-levels: single-entity single-relation, single-entity multi-relation, and multi-entity multi-relation, constructing three sub-models for prediction from the knowledge graph. Additionally, we incorporate signaling pathway data and utilize graph neural network-based methods to construct a pathway sub-model. Finally, we adopt a stacking strategy-based ensemble approach to effectively integrate the prediction results from different sub-models. Based experimental results, our proposed DSESL model outperforms existing state-of-the-art SL prediction methods in all three prediction scenarios. The source code of DSESL is available at https://github.com/TOJSSE-iData/DSESL/.