Semi-supervised learning for potential human microRNA-disease associations inference

By Xing Chen, Guiying Yan. Scientific Reports, 2014

354 citations recorded by the source corpus.

MicroRNAs play critical role in the development and progression of various diseases. Predicting potential miRNA-disease associations from vast amount of biological data is an important problem in the biomedical research. Considering the limitations in previous methods, we developed Regularized Least Squares for MiRNA-Disease Association (RLSMDA) to uncover the relationship between diseases and miRNAs. RLSMDA can work for diseases without known related miRNAs. Furthermore, it is a semi-supervised (does not need negative samples) and global method (prioritize associations for all the diseases simultaneously). Based on leave-one-out cross validation, reliable AUC have demonstrated the reliable performance of RLSMDA. We also applied RLSMDA to Hepatocellular cancer and Lung cancer and implemented global prediction for all the diseases simultaneously. As a result, 80% (Hepatocellular cancer) and 84% (Lung cancer) of top 50 predicted miRNAs and 75% of top 20 potential associations based on global prediction have been confirmed by biological experiments. We also applied RLSMDA to diseases without known related miRNAs in golden standard dataset. As a result, in the top 3 potential related miRNA list predicted by RLSMDA for 32 diseases, 34 disease-miRNA associations were successfully confirmed by experiments. It is anticipated that RLSMDA would be a useful bioinformatics resource for biomedical researches.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d317a7450259cdf3a4bb379ddf2dec8bfad4787

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Zhuhong You
2. Chenggang Clarence Yan
3. Xu Zhang
4. Xing Chen

In Molecular Biology

1. Zhuhong You
2. Chenggang Clarence Yan
3. Xu Zhang
4. Yongdong Zhang
5. Xing Chen

Citations in the corpus

• WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016, methodology): “Based on the assumption that global network similarity measures are better than traditional local network similarity measures in uncovering potential associations between diseases and miRNAs, Chen et al. 37 developed the model of Random Walk with Restart for MiRNA–Disease Association (RWRMDA) to inf”
• MicroRNAs and complex diseases: from experimental results to computational models (2019, neutral): “for inferring human miRNA–disease associations [204].”