摘要
针对基于协同过滤的药物重定位算法进行了研究,考虑到数据稀疏性对协同过滤算法的巨大影响,提出一种基于药物和疾病特征关联的药物重定位混合推荐算法。该算法不仅使用了药物和疾病关系数据,还利用了药物结构、靶蛋白、副作用以及药物—疾病特征矩阵等信息计算药物之间的相似性,降低了数据稀疏性对推荐效果的影响,提高了推荐精度。经过对比实验发现,该算法具备较好的推荐效果,并能够发掘具有潜在联系的药物—疾病组合,进一步验证了该算法可以有效地应用于药物重定位。
This paper studied the algorithm of drug repositioning based on collaborative filtering.Considering the great influence of data sparsity on collaborative filtering algorithm,it proposed a hybrid recommendation algorithm based on the association of drug and disease characteristics.The algorithm not only used the data of drug and disease,but also used the information of drug structure,target protein,side effect and drug-disease feature matrix to calculate the similarity between drugs,which reduced the influence of data sparsity to the recommendation effect and improved the precision of recommendation.The results of contrastive experiment show that the algorithm has a good recommendation effect,and can explore the drug-disease combinations which have potential relationship,and further verified that the algorithm can be effectively applied to drug repositioning.
作者
刘杰
金柳颀
景波
Liu Jie;Jin Liuqi;Jing Bo(Institute of Industry&Equipment Technology,Hefei University of Technology,Hefei 230000,China;National“111 Plan”Gerontechnology Innovate Base,Hefei University of Technology,Hefei 230000,China)
出处
《计算机应用研究》
CSCD
北大核心
2020年第3期672-675,共4页
Application Research of Computers
基金
安徽省2017年度重点研究与开发计划项目(1704e1002221)
国家高等学校学科创新引智“111”计划资助项目(B14025)。
关键词
药物重定位
数据稀疏性
疾病特征
混合推荐
相似度
drug repositioning
data sparsity
disease characteristics
hybrid recommendation
similarity
