A review on methods for predicting miRNA-mRNA regulatory modules.

Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA-mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one's study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

CorGO: An Integrated Method for Clustering Functionally Similar Genes.

Identification of groups of co-expressed or co-regulated genes is critical for exploring the underlying mechanism behind a particular disease like cancer. Condition-specific (disease-specific) gene-expression profiles acquired from different platforms are widely utilized by researchers to get insight into the regulatory mechanism of the disease. Several clustering algorithms are developed using gene expression profiles to identify the group of similar genes. These algorithms are computationally efficient but are not able to capture the functional similarity present between the genes, which is very important from a biological perspective. In this study, an algorithm named CorGO is introduced, that specifically deals with the identification of functionally similar gene-clusters. Two types of relationships are calculated for this purpose. Firstly, the Correlation (Cor) between the genes are captured from the gene-expression data, which helps in deciphering the relationship between genes based on its expression across several diseased samples. Secondly, Gene Ontology (GO)-based semantic similarity information available for the genes is utilized, that helps in adding up biological relevance to the identified gene-clusters. A similarity measure is defined by integrating these two components that help in the identification of homogeneous and functionally similar groups of genes. CorGO is applied to four different types of gene expression profiles of different types of cancer. Gene-clusters identified by CorGO, are further validated by pathway enrichment, disease enrichment, and network analysis. These biological analyses demonstrated significant connectivity and functional relatedness within the genes of the same cluster. A comparative study with commonly used clustering algorithms is also performed to show the efficacy of the proposed method.