Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field

MicroRNAs (miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However, in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data, the decision fusion method is used to prioritize the results of existing methods. After that, the performance of decision fusion method is validated. Furthermore, in consideration of the long range dependencies of successive expression values, Hidden Conditional Random Field model (HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs.