论文标题
NRBDMF：用于预测药物效应的建议算法考虑方向性
NRBdMF: A recommendation algorithm for predicting drug effects considering directionality
论文作者
论文摘要
根据有关批准药物的信息预测药物的新作用可以被视为推荐系统。矩阵分解是最常用的推荐系统之一，为其设计了各种算法。用于预测药物效应的现有算法的文献调查和摘要表明，大多数此类方法，包括邻里正规逻辑矩阵分解，这是基准测试中最佳性能的最佳性能，它使用了仅考虑存在或不存在相互作用的二进制矩阵。但是，已知药物作用具有两个相反的方面，例如副作用和治疗作用。在本研究中，我们建议使用邻域正规化双向基质分解（NRBDMF）通过纳入双向性来预测药物作用，这是药物效应的特征。我们使用这种提出的方法使用矩阵来预测副作用，该基质考虑了药物效应的双向，其中已知的副作用被分配为阳性标签（加1），并为已知的治疗效应分配了阴性（负1）标签。利用药物双向信息的NRBDMF模型在预测列表的底部达到了副作用的富集和指示。第一次尝试使用NRBDMF来考虑药物效应的双向性质的尝试表明，它降低了假阳性并产生了高度可解释的输出。
Predicting the novel effects of drugs based on information about approved drugs can be regarded as a recommendation system. Matrix factorization is one of the most used recommendation systems and various algorithms have been devised for it. A literature survey and summary of existing algorithms for predicting drug effects demonstrated that most such methods, including neighborhood regularized logistic matrix factorization, which was the best performer in benchmark tests, used a binary matrix that considers only the presence or absence of interactions. However, drug effects are known to have two opposite aspects, such as side effects and therapeutic effects. In the present study, we proposed using neighborhood regularized bidirectional matrix factorization (NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic property of drug effects. We used this proposed method for predicting side effects using a matrix that considered the bidirectionality of drug effects, in which known side effects were assigned a positive label (plus 1) and known treatment effects were assigned a negative (minus 1) label. The NRBdMF model, which utilizes drug bidirectional information, achieved enrichment of side effects at the top and indications at the bottom of the prediction list. This first attempt to consider the bidirectional nature of drug effects using NRBdMF showed that it reduced false positives and produced a highly interpretable output.
