College of Artificial Intelligence, Tianjin University of Science and Technology, No. 9, 13th Street, Tianjin Economic-Technological Development Area, Tianjin 300457, China.
College of General Education, Tianjin Foreign Studies University, No. 117, Machang Road, Hexi District, Tianjin 300204, China.
Bioinformatics. 2023 Sep 2;39(9). doi: 10.1093/bioinformatics/btad532.
A critical issue in drug benefit-risk assessment is to determine the frequency of side effects, which is performed by randomized controlled trails. Computationally predicted frequencies of drug side effects can be used to effectively guide the randomized controlled trails. However, it is more challenging to predict drug side effect frequencies, and thus only a few studies cope with this problem.
In this work, we propose a neighborhood-regularization method (NRFSE) that leverages multiview data on drugs and side effects to predict the frequency of side effects. First, we adopt a class-weighted non-negative matrix factorization to decompose the drug-side effect frequency matrix, in which Gaussian likelihood is used to model unknown drug-side effect pairs. Second, we design a multiview neighborhood regularization to integrate three drug attributes and two side effect attributes, respectively, which makes most similar drugs and most similar side effects have similar latent signatures. The regularization can adaptively determine the weights of different attributes. We conduct extensive experiments on one benchmark dataset, and NRFSE improves the prediction performance compared with five state-of-the-art approaches. Independent test set of post-marketing side effects further validate the effectiveness of NRFSE.
Source code and datasets are available at https://github.com/linwang1982/NRFSE or https://codeocean.com/capsule/4741497/tree/v1.
药物获益-风险评估中的一个关键问题是确定副作用的频率，这是通过随机对照试验来完成的。计算预测的药物副作用频率可用于有效地指导随机对照试验。然而，预测药物副作用频率更具挑战性，因此只有少数研究应对了这个问题。
在这项工作中，我们提出了一种邻域正则化方法（NRFSE），利用药物和副作用的多视图数据来预测副作用的频率。首先，我们采用加权非负矩阵分解来分解药物-副作用频率矩阵，其中使用高斯似然来对未知的药物-副作用对进行建模。其次，我们设计了一种多视图邻域正则化方法，分别整合了三种药物属性和两种副作用属性，使得最相似的药物和最相似的副作用具有相似的潜在特征。正则化可以自适应地确定不同属性的权重。我们在一个基准数据集上进行了广泛的实验，NRFSE 提高了与五种最先进方法相比的预测性能。上市后副作用的独立测试集进一步验证了 NRFSE 的有效性。
源代码和数据集可在 https://github.com/linwang1982/NRFSE 或 https://codeocean.com/capsule/4741497/tree/v1 上获得。
