Liang Xujun, Zhang Pengfei, Li Jun, Fu Ying, Qu Lingzhi, Chen Yongheng, Chen Zhuchu
NHC Key Laboratory of Cancer Proteomics, Xiangya Hospital, Central South University, XiangYa Road, Changsha, China.
J Cheminform. 2019 Dec 16;11(1):79. doi: 10.1186/s13321-019-0402-3.
The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.
药物副作用问题是药理学发展中最关键的问题之一。由于当前检测副作用的实验和临床方法存在诸多局限性，人们开发了许多计算算法，利用不同类型的药物信息来预测副作用。然而，仍然缺乏能够整合异构数据以预测副作用并同时选择重要特征的方法。在此，我们提出了一种基于多视图和多标签学习的新型计算框架用于副作用预测。收集了四种不同类型的药物特征，并从每个特征概况构建图模型。之后，将所有单视图图组合起来，对描述药物特征与副作用标签之间关系的线性回归函数进行正则化。对回归系数矩阵施加L1惩罚，以选择与副作用相关的特征。此外，还通过图拉普拉斯正则化将副作用标签之间的相关性纳入模型。实验结果表明，所提出的方法不仅能够为副作用提供更准确的预测，还能够从异构数据中选择与副作用相关的药物特征。还提供了一些案例研究来说明我们的方法在预测药物副作用方面的实用性。
