A new approach for combining knowledge from multiple coexpression networks of microRNAs.

Abstract

MicroRNAs (miRNAs) are a class of small noncoding RNAs that are known to have critical functions across various biological processes. Simultaneous activities of multiple miRNAs can be monitored from their expression profiles under various conditions. We often build up coexpression networks from such profiles. Unfortunately, due to the change of experimental setups (or conditions), the expression profiles do change, and consequently, the patterns of the coexpression networks vary. To obtain a robust functional relationship between miRNAs, by integrating different coexpression networks in a systems biology approach, we have to combine them properly. Here, we evaluate the state-of-the-art techniques and propose a novel integrative measure, and a corresponding methodology, that might be useful for identifying the dependence between coexpression and functional similarity. We establish the results by evaluating the expression profiles of miRNAs taken from bone marrow samples of patients with leukemia. The findings highlight the potential of the integrative algorithm in analyzing the expression profiles of miRNAs for further study.