Distribution of multifunctional enzyme after de-redundance (0.9).

<p>Distribution of multifunctional enzyme after de-redundance (0.9).</p

Distribution of multifunctional enzymes before and after CD-HIT(0.65).

<p>Distribution of multifunctional enzymes before and after CD-HIT(0.65).</p

Distribution of six enzyme classes before and after CD-HIT(0.65).

<p>Distribution of six enzyme classes before and after CD-HIT(0.65).</p

Cross-validation results of Multi-Label classification on multifunctional enzymes only.

<p>Cross-validation results of Multi-Label classification on multifunctional enzymes only.</p

KNN algorithm diagram.

<p>KNN algorithm diagram.</p

Results of fivefeaturerepresentationmethods on IB1 classifier.

<p>Results of fivefeaturerepresentationmethods on IB1 classifier.</p

Cross-validation results of Multi-Label classifiers.

<p>Cross-validation results of Multi-Label classifiers.</p

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

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