Prediction of microRNAs Associated with Human Diseases Based on Weighted <i>k</i> Most Similar Neighbors

<div><p>Background</p><p>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.</p><p>Methodology/Principal Findings</p><p>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 <i>k</i> 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.</p><p>Conclusions</p><p>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 <i>k</i> most similar neighbors. The online prediction and analysis tool is freely available at <a href="http://nclab.hit.edu.cn/hdmpred" target="_blank">http://nclab.hit.edu.cn/hdmpred</a>.</p></div

The disease DAGs of liver neoplasms and pancreatic neoplasms.

<p>(a) DAG of liver neoplasms. (b) DAG of pancreatic neoplasms. The nodes in blue are the disease terms shared by the two DAGs.</p

Prediction results of HDMP and other methods for 5-fold cross validation.

<p>There are 8, 12, and 18 common diseases between HDMP and FCS method, Jiang's method, and RWRMDA, respectively. ‘No. of associated miRNAs’ indicates the number of miRNAs associated with a specific disease in September-2012 Version of HMDD.</p

ROC curves of HDMP and other methods for updated dataset validation.

<p>Each value in bracket is the area under HDMP's ROC curve.</p

Process of predicting disease <i>d</i>-related candidates.

<p>Step 1: calculate the functional similarity of any two miRNAs and construct a symmetric functional similarity matrix. Step 2: assign the members of miRNA family or cluster higher weight. Step 3: calculate the relevance score of each unlabeled miRNA. Step 4: rank all the unlabeled miRNAs according to their scores and select the top ranked miRNAs as potential candidates.</p

Prediction results of HDMP and other methods for updated dataset validation.

<p>There are 4, 7, and 9 common diseases between HDMP and FCS method, Jiang's method, and RWRMDA, respectively. ‘No. of associated miRNAs’ indicates the number of miRNAs associated with a specific disease in November-2010 Version of HMDD. ‘No. of new added miRNAs’ indicates the number of miRNAs associated with a specific disease which are added into HMDD between November-2010 and September-2012.</p

Assigning weight for the members of a miRNA family according to their associations with a group of diseases.

<p>Assigning weight for the members of a miRNA family according to their associations with a group of diseases.</p

ROC curves of HDMP and other methods for 5-fold cross validation.

<p>Each value in bracket is the area under HDMP's ROC curve.</p

Fluorescence polarization binding curves of TDRD3 (aa 520–633), SMN (aa 80–170), and SPF30 (aa 65–150) to the PIWIL1-R4 peptides.

<p>The buffer used in the fluorescence polarization assay is 20 mM Tris pH 7.5, 50 mM NaCl, 1 mM DTT and 0.01% Triton X-100. The data are measured at 25°C and corrected for background by subtracting the free-labeled peptide background. The Kd values are the average of three independent measurements.</p

Binding affinities of TDRD3 (aa 520–633), SMN (aa 80–170), and SPF30 (aa 65–150) to different methyl-arginine peptides in comparison with SND1 (aa 650–910).

<p>NB: No detectable binding.</p><p>*: data from Liu K et al, PNAS, 2010 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0030375#pone.0030375-Liu2" target="_blank">[20]</a>.</p