Department of Physics, Chuo University, Tokyo 112-8551, Japan.
Department of Computer Science, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Eur J Pharm Sci. 2021 May 1;160:105742. doi: 10.1016/j.ejps.2021.105742. Epub 2021 Feb 3.
The accurate prediction of new interactions between drugs is important for avoiding unknown (mild or severe) adverse reactions to drug combinations. The development of effective in silico methods for evaluating drug interactions based on gene expression data requires an understanding of how various drugs alter gene expression. Current computational methods for the prediction of drug-drug interactions (DDIs) utilize data for known DDIs to predict unknown interactions. However, these methods are limited in the absence of known predictive DDIs. To improve DDIs interpretation, a recent study has demonstrated strong non-linear (i.e., dose-dependent) effects of DDIs. In this study, we present a new unsupervised learning approach involving tensor decomposition (TD)-based unsupervised feature extraction (FE) in 3D. We utilize our approach to reanalyze available gene expression profiles for Saccharomyces cerevisiae. We found that non-linearity is possible, even for single drugs. Thus, non-linear dose-dependence cannot always be attributed to DDIs. Our analysis provides a basis for the design of effective methods for evaluating DDIs.
准确预测药物之间的新相互作用对于避免药物组合的未知（轻度或重度）不良反应很重要。基于基因表达数据评估药物相互作用的有效计算方法的开发需要了解各种药物如何改变基因表达。目前用于预测药物-药物相互作用（DDI）的计算方法利用已知的 DDI 数据来预测未知的相互作用。然而，在没有已知的预测性 DDI 的情况下，这些方法受到限制。为了改善 DDI 的解释，最近的一项研究表明 DDI 具有很强的非线性（即剂量依赖性）效应。在这项研究中，我们提出了一种新的基于张量分解（TD）的无监督学习方法，涉及 3D 中的无监督特征提取（FE）。我们利用我们的方法重新分析了酿酒酵母的可用基因表达谱。我们发现即使是单种药物也存在非线性。因此，非线性剂量依赖性并不总是归因于 DDI。我们的分析为设计评估 DDI 的有效方法提供了基础。
