Abstract
Simultaneous mutations in synthetic lethality genes can lead to cancer cell apoptosis and it can be utilized in cancer target therapy. However, high-throughput wet laboratory screening methods are expensive and time-consuming. Computational methods are therefore a good complement to the prediction of synthetic lethality. Recently, graph embedding-based methods have been developed to predict synthetic lethal gene pairs. Here, we proposed a novel synthetic lethality prediction method based on bidirectional attention learning. Through aggregating biological multi-omics data, we can construct the node embedding representation and the graph link representation respectively. The correlation between gene pairs with these two feature representations is calculated using a multilayer perceptron as a decoder. The correlation with high gene pair score is predicted as potential synthetic lethal pair.
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Acknowledgements
This work was supported by Natural Science Foundation of China (Grant No. 61972141) and Natural Science Foundation of Hunan Province, China (Grant No. 2021JJ30144).
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Sun, F., Lu, X., Chen, G., Zhang, X., Jiang, K., Li, J. (2022). A Novel Synthetic Lethality Prediction Method Based on Bidirectional Attention Learning. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_30
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DOI: https://doi.org/10.1007/978-3-031-13829-4_30
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