A deep graph model for the signed interaction prediction in biological network

Shuyi Jin, Mengji Zhang, Meijie Wang, Lun Yu
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Abstract

In pharmaceutical research, the strategy of drug repurposing accelerates the development of new therapies while reducing R&D costs. Network pharmacology lays the theoretical groundwork for identifying new drug indications, and deep graph models have become essential for their precision in mapping complex biological networks. Our study introduces an advanced graph model that utilizes graph convolutional networks and tensor decomposition to effectively predict signed chemical-gene interactions. This model demonstrates superior predictive performance, especially in handling the polar relations in biological networks. Our research opens new avenues for drug discovery and repurposing, especially in understanding the mechanism of actions of drugs.
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用于预测生物网络中签名交互作用的深度图模型
在制药研究中,药物再利用战略可以加速新疗法的开发,同时降低研发成本。网络药理学为确定新药适应症奠定了理论基础,而深度图模型因其在映射复杂生物网络方面的精确性而变得至关重要。我们的研究介绍了一种先进的图模型,它利用图卷积网络和张量分解来有效预测指定的化学基因相互作用。我们的研究为药物发现和再利用开辟了新途径,尤其是在理解药物的作用机制方面。
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