A performance comparison analysis between miRFunSim and other existing methods with similar functions.

<p>(A) The distribution and comparison of functional similarity scores of intrafamily, interfamily, intracluster,intercluster and random miRNA pairs computed by Yu’s method. (B) The distribution and comparison of functional similarity scores of intrafamily, interfamily, intracluster, intercluster miRNA pairs computed by MISIM method. (C) Area under ROC curve (AUC) analysis on 270 high-quality experimentally verified miRNA-disease associations from Jiang’s study and 100 miRNAs whose target genes have been experimentally supported using Yu’s method.</p

Inferring Potential microRNA-microRNA Associations Based on Targeting Propensity and Connectivity in the Context of Protein Interaction Network

<div><p>MicroRNAs (miRNAs) are a group of small non-coding RNAs that play important regulatory roles at the post-transcriptional level. Although several computational methods have been developed to compare miRNAs, it is still a challenging and a badly needed task with the availability of various biological data resources. In this study, we proposed a novel graph theoretic property based computational framework and method, called miRFunSim, for quantifying the associations between miRNAs based on miRNAs targeting propensity and proteins connectivity in the integrated protein-protein interaction network. To evaluate the performance of our method, we applied the miRFunSim method to compute functional similarity scores of miRNA pairs between 100 miRNAs whose target genes have been experimentally supported and found that the functional similarity scores of miRNAs in the same family or in the same cluster are significantly higher compared with other miRNAs which are consistent with prior knowledge. Further validation analysis on experimentally verified miRNA-disease associations suggested that miRFunSim can effectively recover the known miRNA pairs associated with the same disease and achieve a higher AUC of 83.1%. In comparison with similar methods, our miRFunSim method can achieve more effective and more reliable performance for measuring the associations of miRNAs. We also conducted the case study examining liver cancer based on our method, and succeeded in uncovering the candidate liver cancer related miRNAs such as miR-34 which also has been proven in the latest study.</p></div

Performance evaluation of miRFunSim using miRNA family and miRNA cluster.

<p>(A) A comparison of functional similarity scores of intrafamily miRNA pairs, interfamily miRNA pairs and random miRNA pairs. (B) A comparison of functional similarity scores of intracluster miRNA pairs, intercluster miRNA pairs and random miRNA pairs.</p

Area under ROC curve (AUC) analysis on 270 high-quality experimentally verified miRNA-disease associations from Jiang’s study and 100 miRNAs whose target genes have been experimentally supported using our miRFunSim method.

<p>Area under ROC curve (AUC) analysis on 270 high-quality experimentally verified miRNA-disease associations from Jiang’s study and 100 miRNAs whose target genes have been experimentally supported using our miRFunSim method.</p

The top 12 miRNAs with the highest functional similarity scores with known experimentally verified liver cancer-related miRNAs.

<p>Note: The miRNAs which have been recorded to be deregulated in liver cancer in previous studies were designated as “YES”. The miRNAs which have been verified to be deregulated in other cancers in miR2Disease and PhenomiR databases were designated as “*”.</p

The schematic representation and overview of the miRFunSim method.

<p>The schematic representation and overview of the miRFunSim method.</p

The np 3243 MELAS mutation: damned if you aminoacylate, damned if you don't.

The np 3243 MELAS mtDNA mutation in tRNA(leu(UUR))has been variously proposed as a loss-of-function or as a gain-of-function mutation, based on apparently contradictory studies in cultured cell lines. A new report describing the molecular effects of the mutation in vivo now mirrors this variability. This should prompt a more systematic re-investigation of cells carrying the mutation, in order to separate primary from secondary and pathogenic from compensatory effects, all of which may contribute to disease phenotype. Nuclear genetic and developmental background, mitochondrial haplotype, and epigenetic effects may all influence the pathological outcome. Defects in both base-modification and aminoacylation of the mutant tRNA could play critical roles

An overview of the CHNmiRD method.

<p>Firstly, four MFSNs were constructed based on different genomic data by means of miRNA-target relationships and a disease phenotype network was constructed using the information of disease phenotype similarity. Then the complex heterogeneous network was generated by connecting the disease phenotype network and the integrated multigraph MFSN using the known miRNA-disease relationship information. Finally, the predicting miRNA-disease associations were obtained by implementing RWR algorithm on the complex heterogeneous network.</p

The number of successfully predicted miRNAs with different Ns.

<p>The number of successfully predicted miRNAs with different Ns.</p

Performance of individual data source.

<p>Performance of individual data source.</p