To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

中文翻译：

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张量分解在小鼠肝炎病毒感染基因表达中的应用可识别SARS-CoV-2感染的关键人类基因和有效药物

为了更好地了解因感染导致 COVID-19 传染病的新型冠状病毒株 SARS-CoV-2 导致表达改变的基因，将基于张量分解 (TD) 的无监督特征提取 (FE) 方法应用于基因表达谱实验性感染小鼠肝炎病毒的小鼠肝脏和脾脏数据集，被认为是人类冠状病毒感染的合适模型。基于TD的无监督FE选择了134个改变的基因，这些基因富含与orf1ab、多蛋白和3C样蛋白酶的蛋白质-蛋白质相互作用，这些蛋白酶众所周知在冠状病毒感染中发挥着关键作用，这表明这134个基因可以代表冠状病毒的传染性过程。然后，我们根据公共领域数据库选择了针对 134 个选定基因表达的化合物。鉴定出的药物化合物主要与已知的抗病毒药物有关，其中一些药物也包含在之前筛选的药物中。计算机模拟确定治疗 COVID-19 候选药物的方法。