[1]陈 鹏,鲍天嘉智,余肖生.基于多相似度融合的药物重定位推荐算法[J].计算机技术与发展,2021,31(01):168-174.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 030]
　CHEN Peng,BAO Tian-jiazhi,YU Xiao-sheng.Recommendation Algorithm for Drug Repositioning Based onMulti-similarity Fusion[J].,2021,31(01):168-174.[doi:10. 3969 / j. issn. 1673-629X. 2021. 01. 030]
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基于多相似度融合的药物重定位推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年01期
页码:
168-174
栏目:
应用前沿与综合
出版日期:
2021-01-10

文章信息/Info

Title:
Recommendation Algorithm for Drug Repositioning Based onMulti-similarity Fusion
文章编号:
1673-629X(2021)01-0168-07
作者:
陈 鹏; 鲍天嘉智; 余肖生
三峡大学 计算机与信息学院,湖北 宜昌 443002
Author(s):
CHEN Peng; BAO Tian-jiazhi; YU Xiao-sheng
School of Computer and Information,Three Gorges University,Yichang 443002,China
关键词:
药物重定位; 协同过滤; 数据稀疏性; 相似度融合; 预测值融合
Keywords:
drug repositioning; collaborative filtering; data sparsity; similarity fusion; predictive value fusion
分类号:
TP399
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 01. 030
摘要:
药物重定位算法可以为发现药物的新用途提供理论上的支持。 针对传统药物重定位推荐算法的不足,提出一种基于多相似度融合的药物重定位推荐算法( MSF) 。 首先通过药物-疾病数据源计算出疾病相似度,再通过药物-化学结构、药物-靶蛋白以及药物-副作用数据源计算出三种相似度并融合为药物相似度,最后利用两种相似度计算药物与疾病对应关系的预测值,并通过预测值融合方法融合为最终预测值。 实验结果表明,与 SLAMS 算法和 DRCFFS 算法相比,MSF 算法在精确率和召回率上有较大的提高;与针对单个数据源的药物重定位算法相比,通过融合多种数据源,预测值的可靠性和精确性都有进一步的提升。 案例分析显示,MSF 算法可以有效地预测出有治疗效果的药物-疾病组合。
Abstract:
Drug repositioning algorithms can provide theoretical support for discovering new uses of drugs. To overcome the shortcomings of traditional drug relocation recommendation algorithm, we propose a drug repositioning recommendation (MSF) based on multi-similarity fusion. Disease similarity is calculated by drug-disease data sources,and then three similarities are fused into drug similarity by drug-chemical structure, drug-target protein and drug-side effect data sources. Finally, two similarities are used to calculate the predicted value and fuse it into the final predicted value. The experiment shows that compared with SLAMS and DRCFFS,MSF has a greater improvement in accuracy and recall rate. Compared with collaborative filtering algorithm for a single data source, the reliability and accuracy of predicted values are further improved by fusing multiple data sources. Case analysis shows that MSF can effectively predict drug-disease combination with therapeutic effect.

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更新日期/Last Update: 2020-01-10