Graduate School of Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita City, Osaka, 565-0871, Japan.
Sci Rep. 2021 Dec 14;11(1):23942. doi: 10.1038/s41598-021-03348-y.
The severe side effects of some drugs can threaten the lives of patients and financially jeopardize pharmaceutical companies. Computational methods utilizing chemical, biological, and phenotypic features have been used to address this problem by predicting the side effects. Among these methods, the matrix factorization method, which utilizes the side-effect history of different drugs, has yielded promising results. However, approaches that encapsulate all the characteristics of side-effect prediction have not been investigated to date. To address this gap, we applied the logistic matrix factorization algorithm to a database of spontaneous reports to construct a prediction with higher accuracy. We expressed the distinction in the importance of drug-side effect pairs by a weighting strategy and addressed the cold-start problem via an attribute-to-feature mapping method. Consequently, our proposed model improved the prediction accuracy by 2.5% and efficiently handled the cold-start problem. The proposed methodology is expected to benefit applications such as warning systems in clinical settings.
一些药物的严重副作用会威胁到患者的生命，并使制药公司的经济利益面临风险。通过预测副作用，利用化学、生物和表型特征的计算方法已经被用于解决这个问题。在这些方法中，利用不同药物副作用历史的矩阵分解方法已经取得了有希望的结果。然而，迄今为止，还没有研究过包含副作用预测所有特征的方法。为了解决这个差距，我们将逻辑矩阵分解算法应用于自发报告数据库，以构建更高精度的预测。我们通过加权策略表达了药物-副作用对重要性的差异，并通过属性到特征的映射方法解决了冷启动问题。因此，我们提出的模型将预测准确性提高了 2.5%，并且有效地处理了冷启动问题。该方法预计将有益于临床环境中的警告系统等应用。
