Windows

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases. However, the currently known experimental verifications of the disease-miRNA associations are still rare and experimental identification is time-consuming and labor-intensive. Accordingly, identifying potential disease-related miRNAs to help people understand the pathogenesis of complex diseases has become a hot topic.

High-throughput sequencing techniques are increasingly affordable and produce massive amounts of data. Together with other high-throughput technologies, such as microarrays, there are an enormous amount of resources in databases. The collection of these valuable data has been routine for more than a decade. Despite different technologies, many experiments share the same goal. For instance, the aims of RNA-seq studies often coincide with those of differential gene expression experiments based on microarrays. As such, it would be logical to utilize all available data.

MicroRNAs (miRNA) are one of the important regulators of cell division and also responsible for cancer development. Among the discovered miRNAs, not all are important for cancer detection. In this regard a fuzzy mutual information (FMI) based grouping and miRNA selection method (FMIGS) is developed to identify the miRNAs responsible for a particular cancer. First, the miRNAs are ranked and divided into several groups. Then the most important group is selected among the generated groups.

A fast growing number of non-coding RNAs have recently been discovered to play essential roles in many cellular processes. Similar to proteins, understanding the functions of these active RNAs requires methods for analyzing their tertiary structures. However, in contrast to the wide range of structure-based approaches available for proteins, there is still a lack of methods for studying RNA structures.