Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases

By Dong Wang, Juan Wang, Ming Lu, Fei Song, Qinghua Cui. Bioinform., 2010

815 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d104dea1d044126c982ae91d8def14419cc28124

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. Guiying Yan
4. Chenggang Clarence Yan
5. Xu Zhang
6. Qinghua Cui
7. Geng Chen

In Molecular Biology

1. Xing Chen
2. Zhuhong You
3. Guiying Yan
4. Chenggang Clarence Yan
5. Xu Zhang
6. Qinghua Cui
7. Yongdong Zhang
8. Geng Chen

Citations in the corpus

• LncRNADisease: a database for long-non-coding RNA-associated diseases (2012, background): “We previously showed that miRNAs located closely to each other in the genome (particularly miRNAs within 2 kb) and tend to be associated with similar diseases (23,24).”
• Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors (2013, methodology): “The functional similarity of two miRNAs is successfully estimated by the semantic similarity of their associated two groups of diseases [20].”
• WBSMDA: Within and Between Score for MiRNA-Disease Association prediction (2016, methodology): “In previous work 74 , miRNA functional similarity score was calculated based on the assumption that functionally similar miRNAs tend to be associated with phenotypically similar diseases.”
• Semi-supervised learning for potential human microRNA-disease associations inference (2014, methodology): “The human miRNA-disease association dataset used as gold standard dataset in this paper was downloaded from the supplementary material of literature 96 (obtained from HMDD in September, 2009).”