Bio-JOIE：生物知识库的联合表示学习
Bio-JOIE: Joint Representation Learning of Biological Knowledge Bases
摘要：
冠状病毒的广泛传播导致全球大流行，死亡率很高。目前，从不同研究中积累的关于这种病毒的知识非常有限。利用广泛的生物学知识，如基因本体论和其他密切相关物种的蛋白质相互作用（PPI）网络，提出了一个重要的方法来推断一个新物种的分子影响。在本文中，我们提出了转移的多关系嵌入模型Bio-JOIE捕获的基因本体和PPI网络的知识，这表明在建模SARS-CoV-2-人类蛋白质相互作用的卓越能力。Bio-JOIE联合训练两个模型组件。知识模型编码的关系事实从蛋白质和GO域到分离的嵌入空间，使用层次意识的编码技术采用的GO条款。最重要的是，转移模型学习非线性变换，以跨其嵌入空间转移PPI和基因本体注释的知识。通过仅利用结构化知识，Bio-JOIE在多个物种的PPI类型预测方面显着优于现有的最先进方法。此外，我们还展示了利用所学的表征将具有酶功能的蛋白质聚类成酶委员会家族的潜力。最后，我们表明Bio-JOIE可以准确地识别SARS-CoV-2蛋白和人类蛋白之间的PPI，为推进这种新疾病的研究提供了有价值的见解。
英文摘要：
The widespread of Coronavirus has led to a worldwide pandemic with a high mortality rate. Currently, the knowledge accumulated from different studies about this virus is very limited. Leveraging a wide-range of biological knowledge, such as gene ontology and protein-protein interaction (PPI) networks from other closely related species presents a vital approach to infer the molecular impact of a new species. In this paper, we propose the transferred multi-relational embedding model Bio-JOIE to capture the knowledge of gene ontology and PPI networks, which demonstrates superb capability in modeling the SARS-CoV-2-human protein interactions. Bio-JOIE jointly trains two model components. The knowledge model encodes the relational facts from the protein and GO domains into separated embedding spaces, using a hierarchy-aware encoding technique employed for the GO terms. On top of that, the transfer model learns a non-linear transformation to transfer the knowledge of PPIs and gene ontology annotations across their embedding spaces. By leveraging only structured knowledge, Bio-JOIE significantly outperforms existing state-of-the-art methods in PPI type prediction on multiple species. Furthermore, we also demonstrate the potential of leveraging the learned representations on clustering proteins with enzymatic function into enzyme commission families. Finally, we show that Bio-JOIE can accurately identify PPIs between the SARS-CoV-2 proteins and human proteins, providing valuable insights for advancing research on this new disease.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
