Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

By Ping Xuan, Ke Han, Maozu Guo, Yahong Guo, Jinbao Li, Jian Ding, Yong Liu, Qiguo Dai, Jin Li, Zhixia Teng and 1 more. PLoS ONE, 2013

320 citations recorded by the source corpus.

Background The identification of human disease-related microRNAs (disease miRNAs) is important for further investigating their involvement in the pathogenesis of diseases. More experimentally validated miRNA-disease associations have been accumulated recently. On the basis of these associations, it is essential to predict disease miRNAs for various human diseases. It is useful in providing reliable disease miRNA candidates for subsequent experimental studies. Methodology/Principal Findings It is known that miRNAs with similar functions are often associated with similar diseases and vice versa. Therefore, the functional similarity of two miRNAs has been successfully estimated by measuring the semantic similarity of their associated diseases. To effectively predict disease miRNAs, we calculated the functional similarity by incorporating the information content of disease terms and phenotype similarity between diseases. Furthermore, the members of miRNA family or cluster are assigned higher weight since they are more probably associated with similar diseases. A new prediction method, HDMP, based on weighted k most similar neighbors is presented for predicting disease miRNAs. Experiments validated that HDMP achieved significantly higher prediction performance than existing methods. In addition, the case studies examining prostatic neoplasms, breast neoplasms, and lung neoplasms, showed that HDMP can uncover potential disease miRNA candidates. Conclusions The superior performance of HDMP can be attributed to the accurate measurement of miRNA functional similarity, the weight assignment based on miRNA family or cluster, and the effective prediction based on weighted k most similar neighbors. The online prediction and analysis tool is freely available at http://nclab.hit.edu.cn/hdmpred.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/f6b06bcdb3972a9b6e5825a663232609a48f3eeb

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In Cancer Research

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

In Molecular Biology

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

Citations in the corpus

• Semi-supervised learning for potential human microRNA-disease associations inference (2014, methodology): “For the HDMP, global LOOCV can be implemented.”