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 achieved 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.
Highlights
MiRNAs are a group of short non-coding RNAs that mediate post-transcriptional gene silencing[1]
We develop a novel method to discover potential miRNA-disease associations based on Adaptive Multi-View Multi-Label learning
The methods introduced above mainly predicted disease-related miRNAs by applying random walk algorithms to the reconstructed similarity networks[9]. Another family of prediction methods was generally based on network topological characteristics and achieved remarkable performance
Summary
MiRNAs are a group of short non-coding RNAs that mediate post-transcriptional gene silencing[1]. Chen et al measured the global network similarity and inferred potential miRNA-disease interactions based on random walk with restart[4]. The methods introduced above mainly predicted disease-related miRNAs by applying random walk algorithms to the reconstructed similarity networks[9]. Another family of prediction methods was generally based on network topological characteristics and achieved remarkable performance. Chen et al computed the association possibility between a disease node and a miRNA node in the corresponding graphlet interaction isomers[14] Effective, these methods are sensitive to the change of the network topological structures, which might affect the prediction accuracy. Chen et al extracted novel feature vectors for both miRNAs and diseases to train a random forest classifier for the prediction task[22]
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