In silico drug repositioning using deep learning and comprehensive similarity measures | |
Yi, HC (Yi, Hai-Cheng) 1 , 2; You, ZH (You, Zhu-Hong) 1; Wang, L (Wang, Lei) 1; Su, XR (Su, Xiao-Rui) 1 , 2; Zhou, X (Zhou, Xi) 1; Jiang, TH (Jiang, Tong-Hai) 1 | |
刊名 | BMC BIOINFORMATICS |
2021 | |
卷号 | 22期号:增刊3页码:1-14 |
关键词 | Drug repositioningDrug-disease interactionGated recurrent unitsGaussian interaction profile kernelMachine learning |
DOI | 10.1186/s12859-020-03882-y |
英文摘要 | Background: Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for drug repositioning based on similarity measures among drugs and diseases. However, there are some known associations between drugs and diseases that previous studies not utilized. Methods: In this work, we develop a deep gated recurrent units model to predict potential drug-disease interactions using comprehensive similarity measures and Gaussian interaction profile kernel. More specifically, the similarity measure is used to exploit discriminative feature for drugs based on their chemical fingerprints. Meanwhile, the Gaussian interactions profile kernel is employed to obtain efficient feature of diseases based on known disease-disease associations. Then, a deep gated recurrent units model is developed to predict potential drug-disease interactions. Results: The performance of the proposed model is evaluated on two benchmark datasets under tenfold cross-validation. And to further verify the predictive ability, case studies for predicting new potential indications of drugs were carried out. Conclusion: The experimental results proved the proposed model is a useful tool for predicting new indications for drugs or new treatments for diseases, and can accelerate drug repositioning and related drug research and discovery. |
WOS记录号 | WOS:000656873000001 |
内容类型 | 期刊论文 |
源URL | [http://ir.xjipc.cas.cn/handle/365002/7851] |
专题 | 新疆理化技术研究所_多语种信息技术研究室 |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Xinjiang Tech Inst Phys & Chem, Urumqi 830011, Peoples R China |
推荐引用方式 GB/T 7714 | Yi, HC ,You, ZH ,Wang, L ,et al. In silico drug repositioning using deep learning and comprehensive similarity measures[J]. BMC BIOINFORMATICS,2021,22(增刊3):1-14. |
APA | Yi, HC ,You, ZH ,Wang, L ,Su, XR ,Zhou, X ,&Jiang, TH .(2021).In silico drug repositioning using deep learning and comprehensive similarity measures.BMC BIOINFORMATICS,22(增刊3),1-14. |
MLA | Yi, HC ,et al."In silico drug repositioning using deep learning and comprehensive similarity measures".BMC BIOINFORMATICS 22.增刊3(2021):1-14. |
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