摘要
药物不良反应(drug adverse reactions,ADRs)的早期准确鉴定对药物研发和临床用药安全具有重要的实际意义。基于计算机辅助预测药物的不良反应已经引起了越来越多的重视。将一种核矩阵降维(kernel matrix dimension reduction,KMDR)算法应用于药物不良反应的统计预测中,考察该方法在药物不良反应上的预测性能。通过与其他两种参考算法在相同标准数据集上进行交叉验证和独立测试的计算实验表明,KMDR算法是一种值得推广的预测药物不良反应的候选统计算法。
Drug adverse reactions(ADRs)are quite important in drug discovery and clinical drug safety.Predicting ADRs by machine learning methods has been attracting more and more attentions.Here,a kernel matrix dimension reduction(KMDR)method was applied to infer drug ADRs and the predictive performance of this method in ADRs was investigated.The cross validation and independent tests are performed on the same standard data set with the other two reference algorithms.The results suggest that the KMDR methodcan be a promising method for ADRs prediction.
出处
《中国科技论文》
北大核心
2017年第24期2845-2849,共5页
China Sciencepaper
基金
高等学校博士学科点专项科研基金资助项目(20120181110051)
关键词
化学信息学
药物不良反应预测
核矩阵降维算法
cheminformatics
drug adverse reactions prediction
kernel matrix dimension reduction method
