iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Briefings in Bioinformatics - Tập 21 Số 4 - Trang 1356-1367 - 2020
Hang Wei1, Bin Liu2,1
1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, Guangdong, China
2School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

Tóm tắt

AbstractCircular RNAs (circRNAs) are a group of novel discovered non-coding RNAs with closed-loop structure, which play critical roles in various biological processes. Identifying associations between circRNAs and diseases is critical for exploring the complex disease mechanism and facilitating disease-targeted therapy. Although several computational predictors have been proposed, their performance is still limited. In this study, a novel computational method called iCircDA-MF is proposed. Because the circRNA-disease associations with experimental validation are very limited, the potential circRNA-disease associations are calculated based on the circRNA similarity and disease similarity extracted from the disease semantic information and the known associations of circRNA-gene, gene-disease and circRNA-disease. The circRNA-disease interaction profiles are then updated by the neighbour interaction profiles so as to correct the false negative associations. Finally, the matrix factorization is performed on the updated circRNA-disease interaction profiles to predict the circRNA-disease associations. The experimental results on a widely used benchmark dataset showed that iCircDA-MF outperforms other state-of-the-art predictors and can identify new circRNA-disease associations effectively.

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Briefings in Bioinformatics

Tập 21 Số 4

1356-1367

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