论文标题
DeepVir-用于“在硅中”抗病毒重新定位的图形深矩阵分解：应用于COVID-19
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19
论文作者
论文摘要
这项工作将抗病毒重新定位作为基质完成问题，其中抗病毒药物沿行沿着柱子沿着柱子和病毒。输入矩阵被部分填充，其位于已知抗病毒对病毒有效的位置。用于抗病毒药（化学结构和途径）以及病毒（基因组结构和症状）的策划元数据被编码为我们的基质完成框架，作为图形laplacian正则化。然后，我们将所得的多个图矩阵完整问题作为深矩阵分解。这是通过使用一种称为Hypalm（杂交近端交替线性化最小化）的新型优化方法来解决的。我们策划的RNA药物病毒关联（DVA）数据集的结果表明，所提出的方法在最先进的图形正则矩阵完成技术上擅长。当将抗病人的抗病毒药预测应用于COVID-19时，我们的方法将返回用于治疗患者或接受试验的抗病毒药。
This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques. When applied to "in silico" prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under for trials for the same.
