药物-疾病关系预测:一种推荐系统模型  

Mining drug-disease relationships: a recommendation system

作　　者：汪浩[1] 王海平[1] 吴信东[1,3] 刘琦[2] 

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009 [2]同济大学生命科学与技术学院,上海200092 [3]佛蒙特大学计算机系,美国伯灵顿05405

出　　处：《中国药理学通报》2015年第12期1770-1774,共5页Chinese Pharmacological Bulletin

基　　金：国家自然科学基金资助项目(No 31100956,61173117);国家高技术研究发展计划(863计划)资助项目(No2012AA020405)

摘　　要：目的药物重定位是指发掘已有药物新的治疗作用,然而具有潜在治疗作用的药物-疾病往往隐藏在数以百万计的关系对中。该研究基于医疗大数据分析,预测具有潜在治疗关系的药物-疾病关系对。方法将社交网络中推荐系统模型应用于药物重定位研究,并假设具有相似化学结构的药物可能具有相似的适应症。从开源数据库收集已知药物-疾病的治疗关系、副作用关系以及药物和疾病特征描述符,计算得到药物-药物的相似度和疾病-疾病相似度,再构建推荐模型将上述信息融合,并预测具有潜在治疗关系的药物-疾病,最终得到预测关系对的排序列表。结果列表排名前500的关系对中,有12.8%得到临床实验支持或综述报道,20%得到模式生物实验或细胞实验支持。结论相比于已有分类模型和随机抽样结果,本模型可明显提高具有潜在治疗作用药物-疾病的富集程度。Aim Drug repositioning is to find new indications for existing drugs,however,potential drug-disease relationships are often hidden in millions of unknown relationship. With the analyzing of medical big data,we predict the potential drug-disease relationships. Methods Based on the assumption that similar drugs tend to have similar indications,we applied a recommendation-based strategy to drug repositioning. First,we collected the information of known drug-disease therapeutic effect,side effect,drug characters and disease characters; second,we calculated the drug-drug similarity measurements and diseasedisease similarity measurements; last,we used a collaborative filtering( CF) method to predict unknown drug-disease relationships based on the known drug-disease relationships by integrating the similarity measurements,and built a ranking list of prediction results. Results The experiments demonstrated that among the TOP 500 of the list,12. 8% were supported by clinical experiments or review,and 20% were supported by model organism or cell experiments. Conclusion Compared to the classification model and random sampling results,the CF model can effectively reduce the false positives.

关 键 词：药物重定位 医药大数据 推荐系统 相似性度量 协同过滤 药物和疾病关系预测 机器学习 

分 类 号：R-05[医药卫生] R195