RWRMDA: predicting novel human microRNA-disease associations.

By Xing Chen, Mingxi Liu, Guiying Yan. Molecular Biosystems, 2012

404 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1c95973de56f7f90c74604f4cf78798d91f9de9c

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. Xing Chen
2. Zhuhong You
3. Chenggang Clarence Yan
4. Xu Zhang
5. Guiying Yan
6. Qinghua Cui
7. Jia Qu
8. Di Xie

In Molecular Biology

1. Xing Chen
2. Zhuhong You
3. Chenggang Clarence Yan
4. Xu Zhang
5. Guiying Yan
6. Qinghua Cui
7. Di Xie
8. Qi Zhao

Citations in the corpus

• MicroRNAs and complex diseases: from experimental results to computational models (2019, positive): “First, disease module principle (similar diseases tend to be related with functional similar miRNAs) could be used to predict novel disease– miRNA associations based on known experimentally verified associations [37, 64].”
• Predicting miRNA‐disease association based on inductive matrix completion (2018, methodology): “Chen and Yan (2015) presented a semi-supervised learning-based model of Regularized Least Squares for MiRNA–Disease Association (RLSMDA) based on semi-supervised learning framework so that negative samples were not required in this model. The drawback of RLSMDA is how to choose the appropriate param”
• WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016, background): “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”
• HMDD v2.0: a database for experimentally supported human microRNA and disease associations (2013, methodology): “Based on the miRNA functional similarity, two other labs developed network-based methods to predict novel miRNA-associated diseases (24,25).”
• Semi-supervised learning for potential human microRNA-disease associations inference (2014, methodology): “To our knowledge, RWRMDA 47 , HDMP 52 , and the global network algorithm developed by Shi et al. 51 are the-state-of-art approaches in the computational research about disease-related miRNA prediction.”
• Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors (2013, methodology): “The restart probability r of RWRMDA is set to 0.9 suggested by the experiments in [22].”