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.
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Infers miRNA mediate regulation from co-expression signatures. FIRM was used to build a comprehensive miRNA regulatory network that links about 1300 coexpression signatures to post-transcriptional regulation mediated by about 600 miRNAs. It is based on a combination of three algorithms that use complementary strategies for inference of miRNA regulatory networks. This tool integrates disparate data types such as gene coexpression and distributions of both known and de novo-discovered miRNA-binding sites.
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.
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.
A data integration framework to identify the miRNA-gene regulatory co-modules. SNMNMF integrates miRNA and gene expression profiles from the same patients, miRNA-gene networks, and gene interaction networks. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm.
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.
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.
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.
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.
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.
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.
Integrates proteomic and mRNA expression data together to infer miRNA-centered regulatory networks. With both types of high-throughput data from the users, ProteoMirExpress is able to discover not only miRNA targets that have decreased mRNA, but also subgroups of targets with suppressed proteins whose mRNAs are not significantly changed or with decreased mRNA whose proteins are not significantly changed, which are usually ignored by most current methods.
An online tool for identification of micorRNA-mediated module which is formed by protein-protein interactions (PPIs). Mirin integrates microRNA regulations and PPIs with microRNA/mRNA expression data to identify the perturbed microRNA regulatory modules and reveals their functional roles in specific biological conditions.
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Assists in reducing the number of miRNAs that are regulating a specific gene. JBCB is a statistic approach that is available under a Microsoft Excel file. The tables are shown in different Excel Sheets named “miRNA & targets(4+)”, “miRNA & targets(3)”, “miRNA & targets(2)” and “miRNA & targets(1)”. Highly homologous genes have been removed to avoid forming co-regulated gene groups among themselves.
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