期刊
BMC MEDICAL GENOMICS
卷 13, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12920-020-0679-0
关键词
CircRNA-disease associations; CircRNA sequence similarity; Disease semantic similarity; Inductive matrix completion
资金
- National Natural Science Foundation of China [61672037, 21601001, U19A2064, 11835014]
- Anhui Provincial Outstanding Young Talent Support Plan [gxyqZD2017005]
- Young Wanjiang Scholar Program of Anhui Province
- Key Project of Anhui Provincial Education Department [KJ2017ZD01]
- China Postdoctoral Science Foundation [2018 M630699]
- Anhui Provincial Postdoctoral Science Foundation [2017B325]
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.
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