Prediction of circRNA-disease associations based on inductive matrix completion

HFCAS OpenIR

	Prediction of circRNA-disease associations based on inductive matrix completion
其他题名	Prediction of circRNA-disease associations based on inductive matrix completion
	Li, Menglu 1; Liu, Mengya 2; Bin, Yannan 1; Xia, Junfeng 1
	2020
发表期刊	BMC MEDICAL GENOMICS
ISSN	1755-8794
摘要	Background Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies. Results Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation. Conclusion All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.
其他摘要	Background Currently, numerous studies indicate that circular RNA (circRNA) is associated with various human complex diseases. While identifying disease-related circRNAs in vivo is time- and labor-consuming, a feasible and effective computational method to predict circRNA-disease associations is worthy of more studies. Results Here, we present a new method called SIMCCDA (Speedup Inductive Matrix Completion for CircRNA-Disease Associations prediction) to predict circRNA-disease associations. Based on known circRNA-disease associations, circRNA sequence similarity, disease semantic similarity, and the computed Gaussian interaction profile kernel similarity, we used speedup inductive matrix completion to construct the model. The proposed SIMCCDA method obtains an area under ROC curve (AUC) of 0.8465 with leave-one-out cross validation in the dataset, which is obtained by the combination of the three databases (circRNA disease, circ2Disease and circR2Disease). Our method surpasses other state-of-art models in predicting circRNA-disease associations. Furthermore, we conducted case studies in breast cancer, stomach cancer and colorectal cancer for further performance evaluation. Conclusion All the results show reliable prediction ability of SIMCCDA. We anticipate that SIMCCDA could be utilized to facilitate further developments in the field and follow-up investigations by biomedical researchers.
语种	英语
资助项目	[National Natural Science Foundation of China] ; [Anhui Provincial Outstanding Young Talent Support Plan] ; [Young Wanjiang Scholar Program of Anhui Province] ; [Key Project of Anhui Provincial Education Department] ; [China Postdoctoral Science Foundation] ; [Anhui Provincial Postdoctoral Science Foundation]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/124222
专题	中国科学院合肥物质科学研究院
作者单位	1.Anhui University 2.Hefei Institutes of Physical Science, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Li, Menglu,Liu, Mengya,Bin, Yannan,et al. Prediction of circRNA-disease associations based on inductive matrix completion[J]. BMC MEDICAL GENOMICS,2020,13.
APA	Li, Menglu,Liu, Mengya,Bin, Yannan,&Xia, Junfeng.(2020).Prediction of circRNA-disease associations based on inductive matrix completion.BMC MEDICAL GENOMICS,13.
MLA	Li, Menglu,et al."Prediction of circRNA-disease associations based on inductive matrix completion".BMC MEDICAL GENOMICS 13(2020).