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Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.
- Source :
- PLoS Computational Biology; 4/1/2019, Vol. 15 Issue 4, p1-18, 18p, 1 Diagram, 2 Charts, 5 Graphs
- Publication Year :
- 2019
-
Abstract
- Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease associations, they suffer from various limitations that affect the prediction accuracy and their applicability. In this study, we develop a novel method to discover disease-related candidate miRNAs based on Adaptive Multi-View Multi-Label learning(AMVML). Specifically, considering the inherent noise existed in the current dataset, we propose to learn a new affinity graph adaptively for both diseases and miRNAs from multiple similarity profiles. We then simultaneously update the miRNA-disease association predicted from both spaces based on multi-label learning. In particular, we prove the convergence of AMVML theoretically and the corresponding analysis indicates that it has a fast convergence rate. To comprehensively illustrate the prediction performance of our method, we compared AMVML with four state-of-the-art methods under different validation frameworks. As a result, our method achieves comparable performance under various evaluation metrics, which suggests that our method is capable of discovering greater number of true miRNA-disease associations. The case study conducted on thyroid neoplasms further identified a potential diagnostic biomarker. Together, the experimental results confirms the utility of our method and we anticipate that our method could serve as a reliable and efficient tool for uncovering novel disease-related miRNAs. [ABSTRACT FROM AUTHOR]
- Subjects :
- MICRORNA
CARCINOGENESIS
SEMANTICS
TUMORS
APPLIED mathematics
PERFORMANCE evaluation
Subjects
Details
- Language :
- English
- ISSN :
- 1553734X
- Volume :
- 15
- Issue :
- 4
- Database :
- Complementary Index
- Journal :
- PLoS Computational Biology
- Publication Type :
- Academic Journal
- Accession number :
- 135656098
- Full Text :
- https://doi.org/10.1371/journal.pcbi.1006931