miRDRN-miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks.

Background: MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein-protein interactions are available in public databases.

Gene-Set Local Hierarchical Clustering (GSLHC)--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups.

Gene-set-based analysis (GSA), which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA), which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap), an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode.

ncRNAppi--a tool for identifying disease-related miRNA and siRNA targeting pathways.

Unlabelled: Currently, there are a number of databases which store microRNA (miRNA) information, and tools available which provide miRNA target prediction. In this article, we describe a novel web-based tool that integrate the miRNA-targeted mRNA data, protein-protein interactions (PPI) records, tissues, biochemical pathways, human disease and gene function information to establish a disease-related miRNA target pathway database. This database is unique in the sense that it links miRNA target genes with their PPI partners according to being tissue- and diseases-specific or both.

Applications of domain-domain interactions in pathway study.

The domain combination pair approach is employed to derive putative protein domain-domain interactions (DDI) from the protein-protein interactions (PPI) database DIP. The results of putative DDI are computed for seven species. To determine the prediction performance, putative DDI results are compared with that of the database InterDom, where an average matching ratio of about 76% can be achieved.