Recognizing the modular organization of biological networks has greatly advanced our understanding of complex cellular systems. However, little is known about the modules that exist in miRNA-gene regulation systems, and even less is known about these modules’ role in specific biological processes and key regulation assemblies. Identifying functional miRNA-gene regulatory modules is a challenging task for several reasons. (i) One gene can be regulated by multiple miRNAs, and one miRNA can regulate a large number of genes. (ii) The miRNA–mRNA target relationships differ among tissues and conditions. (iii) Although miRNAs physically interact with mRNAs, ultimately miRNA regulation affects the quantities of proteins in cells rather than the quantities of mRNAs. (iv) The genomic data are generally noisy and incomplete.
Hosts a network-assisted hypothesis-generating server for C. elegans. WormNet includes a base gene network, which substantially improved predictions of RNAi phenotypes. The server generates various gene network-based hypotheses using three complementary network methods: (i) a phenotype-centric approach to ‘find new members for a pathway’; (ii) a gene-centric approach to ‘infer functions from network neighbors’ and (iii) a context-centric approach to ‘find context-associated hub genes’, which is a method to identify key genes that mediate physiology within a specific context.
Allows users for in-depth analysis of individual miRNAs in the context of synergistic surroundings. MFSN is a computational method to identify significantly functional synergistic miRNA pairs via functional modules which are jointly regulate by integrating predicted miRNA targets. As general biological networks, the MFSN is scale free, modular and has a small-world property.
Allows measurement of pathway activity and crosstalk among pathways. StarBioTrek was developed for the identification of a network of dependent pathways and their regulatory miRNAs. It was used for revealing microRNA (miRNAs) that can regulate, in a coordinated way, networks of gene pathways and permitted to identify pairwise pathways for breast cancer (BC) subtypes able to discriminate BC versus normal samples.
Analyzes and visualizes the expression, variation and correlation of a gene set in cancers with flexible manner. GSCALite is an interactive web-based application that offers analyses including gene differential expression, overall survival, single nucleotide variation, copy number variation, methylation, pathway activity, miRNA regulation, normal tissue expression and drug sensitivity. It also provides genotype-tissue expression (GTEx) normal tissue module for gene set tissue specificity analysis.
Serves for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. SNMNMF is a program that presents the following characteristics: (1) integrates prior knowledge such as a gene interaction network as a constraint on the solution space; (2) allows users to upload several different types of data; (3) and provides sparse solutions. Moreover, this tool can be used for many problems involving heterogeneous data sources.
Permits to identify miRNA modules from their predicted miRNA targets as well as their expression data sets. The algorithm of Joung et al. is a population-based probabilistic search method. This model facilitates the incorporation of multiple heterogeneous information sources by adopting a balanced fitness function and co-evolutionary learning strategies. It also found miRNA–mRNA modules with significantly high fitness scores.
Permits to identify important patterns hidden in the complex interactions. The algorithm of Yoon and De Micheli is a computational method to predict miRNA regulatory modules (MRMs) or groups of miRNAs and their targets that are believed to participate cooperatively in post-transcriptional gene regulation. This method provides groups of miRNAs and co-targeted genes automatically. This method consists of five major steps: (i) target identification, (ii) relation graph representation, (iii) seed finding, (iv) merging seeds to find candidate modules and (v) post-processing.
Allows discovering the functional miRNA regulatory modules (FMRMs). The algorithm of Liu et al. is a method that integrates heterogeneous datasets, including expression profiles of both miRNAs and mRNAs, with or without using prior target binding information. This model is inspired by the Correspondence Latent Dirichlet Allocation (Corr-LDA), which has been used to extract the correspondence patterns from heterogeneous data. This method simultaneously identifies groups of interactive miRNAs and mRNAs, which are believed to participate in specific biological functions.
Detects synergistic miRNA regulatory modules. Mirsynergy operates in two stages: it first forms microRNA regulatory modules (MiRMs) based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions.
Extends the regression framework of GroupMiR by using an additional type of data: protein interactions. PIMiM is a method for inferring condition-specific regulation of miRNAs and for identifying their targets. It combines sequence, expression and interaction data to discover miRNA-regulated modules of mRNAs. PIMiM can already be of use to researchers that collect mRNA and miRNA expression data.
Detects the role of miRNAs in various interesting biological processes and environmental stimuli. The algorithm of Joung and Fei provides a framework to efficiently hypothesize associations between miRNAs and different phenotypes influenced by them. This approach could contribute to elucidation of the gene regulatory program related to functional modules of miRNAs in many additional species for which genome sequences and comprehensive expression datasets are available.
Constructs a comprehensive set of predicted transcription factor (TF)-miRNA interactions for use in systems biology applications. This method is based on semi-supervised learning approaches that integrates sequence, conservation, expression and ChIP-Seq data. It employs graphs to reassign labels to unobserved potential interactions. This approach can be used to improve methods for predicting TF-miRNA regulation.
Allows users to determine and visualize disease-specific miRNA-miRNA interactions for diseases recorded in PhenomiR database. miRsig is a consensus-based network inference method that authorizes for finding out a common/core miRNA-miRNA interaction component among a selection of diseases composed by the user. Results can be downloaded and further be used in network analysis tools.
Identifies miRNA-mRNA regulatory modules (MMRMs) and hence reveals miRNA-mRNA regulatory relationships from heterogeneous data. DICORE is an effective computational framework to reveal correct group information with structural link information and the strength of collective relationships. The overall workflow comprises a data pre-processing step and two main stages: (i) forming separate miRNA and mRNA groups and (ii) searching for COREs.
A tool to discover patterns of miRNAs and their target genes, observed frequently up- or down-regulated in a group of patients. iSubgraph is capable to detect cooperative regulation of miRNAs and genes even if it occurs in some patients only. These miRNA-mRNA modules were used in a stable unsupervised class prediction model for patient stratification to discover HCC subgroups.
Incorporates heterogeneous information to discover biologically relevant miRNA–mRNA groups. The algorithm of Liu et al. is a computational method for identifying functional regulatory miRNA–mRNA modules using predicted miRNA targets as well as expression profiles of miRNAs and mRNAs. This script models miRNA–mRNA regulatory modules (MRMs) that help to understand complex biological procedures.
Performs a comprehensive analysis of the combinatorial nature of gene regulation by detecting rules that identify a set of miRNAs associated with genes. The algorithm of Tran et al. is a computational method for finding miRNA regulatory modules (MRMs) from their predicted target genes and expression datasets (mRNA expression profiles and miRNA expression profiles). The method extracts IFTHEN rules of miRNA combinations shared by target genes with a common expression profile.
Performs miRNA-transcription factor (TF) co-regulation analysis in the context of cancer research. CMTCN collects and integrates the published regulatory relationships among miRNAs, TFs, and target genes from 11 databases and provides a means of curating cancer specific interactions by referring to documented cancer-related gene and miRNA repositories. It can serve to find candidate cancer genes, and interpret the integrative global effects of TFs and miRNAs.
Infers functional miRNA– mRNA regulatory modules (FMRMs), which consist of miRNAs, mRNAs and expression types in each biological class type. The algorithm of Zhang et al. is a probabilistic model, which first uses gene differential expression analysis to find differentially expressed miRNAs and mRNAs, and then infers FMRMs in specific cellular conditions with a probabilistic topic model. This model is closely associated with the latent Dirichlet allocation (LDA) model.
A method to predict miRNA regulatory modules (MRMs) based on bicliques merging. BCM exploits the miRNA/mRNA expression profiles, target site predictions as well as the gene-gene interactions. Furthermore, the module number is automatically determined during the merging process. We apply our method to breast cancer and thyroid cancer datasets downloaded from TCGA. Comparing with alternative formalisms, we show that the modules identified by our method are more densely connected and functionally enriched.