Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:1731-1734. doi: 10.1109/EMBC46164.2021.9630716.
Synthetic lethality (SL) is currently one of the most effective methods to identify new drugs for cancer treatment. It means that simultaneous inactivation target of two non-lethal genes will cause cell death, but loss of either will not. However, detecting SL pair is challenging due to the experimental costs. Artificial intelligence (AI) is a low-cost way to predict the potential SL relation between two genes. In this paper, a new Multi-Graph Ensemble (MGE) network structure combining graph neural network and existing knowledge about genes is proposed to predict SL pairs, which integrates the embedding of each feature with different neural networks to predict if a pair of genes have SL relation. It has a higher prediction performance compared with existing SL prediction methods. Also, with the integration of other biological knowledge, it has the potential of interpretability.
合成致死性（SL）是目前鉴定癌症治疗新药物最有效的方法之一。它意味着同时失活两个非致死性基因的靶标会导致细胞死亡，但失去任何一个都不会。然而，由于实验成本高，检测 SL 对是具有挑战性的。人工智能（AI）是一种低成本的方法，可以预测两个基因之间潜在的 SL 关系。在本文中，提出了一种新的多图集成（MGE）网络结构，结合图神经网络和现有基因知识，用于预测 SL 对，该结构将每个特征的嵌入与不同的神经网络相结合，以预测一对基因是否具有 SL 关系。与现有的 SL 预测方法相比，它具有更高的预测性能。此外，通过整合其他生物学知识，它具有可解释性的潜力。
