1 - 50 of 277 results

CMGRN / Constructing Multilevel Gene Regulatory Networks

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An integrative web server to unravel hierarchical interactive networks at different regulatory levels. The developed method used the Bayesian network modeling to infer causal interrelationships among transcription factors or epigenetic modifications by using ChIP-seq data. Moreover, CMGRN used Bayesian hierarchical model with Gibbs sampling to incorporate binding signals of these regulators and gene expression profile together for reconstructing gene regulatory networks.

RGBM / Regularized Gradient Boosting Machines

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Supplies an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data. RGBM is a gene regulatory network (GRN) inference algorithm. It concerns data from heterogeneous information sources about dynamic time-series, gene knockout, gene knockdown, DNA microarrays and RNA-Seq expression profiles. It can identify the main regulators of the molecular subtypes of brain tumors.

BANJO / BAyesian Network inference with Java Objects

Deciphers gene regulatory networks (GRNs) from gene expression data. BANJO permits users to infer putative information flow networks in the brain from microelectrode array data. It can handle nonlinearity of real brain electrophysiology data. In this tool, the inferred neural flow is appropriately constrained to the anatomical connectivity network. It matches physiological features of the expected flow network, and it is consistent with the measured temporal dynamics of the system.

TSNI / Time Series Network Identification

Allows users to recover direct targets of the transcription factor TRP63, rather than the whole gene network. TSNI uses dynamic gene expression profiles to elucidate the function of a transcription factor and to infer its direct targets. It is able to produce an estimated precision of about 60 per cent. This tool is complementary to genome-wide ChIP-chip approach, which requires the availability of specific antibodies and that is unable to distinguish between functional and nonfunctional target sequences.

DREM / Dynamic Regulatory Events Miner

Integrates static interaction data of dynamic regulatory networks with time series gene expression leading to models that can determine when transcription factors (TFs) activate genes and what genes they regulate. DREM accepts continuous binding values and utilizes TF expression levels when searching for dynamic models. It can discriminative motif discovery, which is particularly powerful for species with limited experimental interaction data. The tool uses time series expression data and static interaction data which is often condition-independent.

BNFinder / Bayesian Network Finder

Provides a more comprehensive method for inferring networks with predefined error rate. BNFinder is a flexible tool for network topology learning from experimental data. It also introduces the possibility of calculating the optimal networks under the Mutual Information Test (MIT) score adapted to handle continuous variables as well as discrete ones. It can be also used for classification tasks. Finally, BNFinder can use parallelization on muliplecore machines to greatly improve the running times of Bayesian Networks (BNs) learning.


Allows inferring gene regulatory networks with direct physical interactions from microarray expression data. For systems biology to succeed in the long run, it is of crucial importance to establish methods that extract large-scale gene networks from high-throughput data that reflect the underlying causal interactions among genes or gene products. C3NET can contribute to this endeavor by demonstrating that an inference algorithm with a neat design permits not only a more intuitive and possibly biological interpretation of its working mechanism but can also result in superior results.

MERLIN / Modular regulatory network learning with per gene information

Infers regulatory programs for individual genes while probabilistically constraining these programs to reveal module-level organization of regulatory networks. MERLIN is an algorithm for learning regulatory networks that strikes a balance between per-gene and per-module methods. It was used to dissect global transcriptional behavior in two biological contexts: yeast stress response and human embryonic stem cell differentiation.

GAGE / Generally Applicable Gene-set Enrichment

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Allows gene set or pathway analysis. GAGE is able to reveal novel and relevant regulatory mechanisms from microarray studies. It produces good results concerning consistency across parallel studies or experiments; sensitivity and specificity of the pathway inference and biological relevance of the pathways identified. The tool compare expression level changes of a gene sets to the whole set background by using a two-sample t-test.

COGRIM / Clustering Of Genes into Regulons using Integrated Modeling

Allows prediction of target gene. COGRIM is based on the integration of serum response factor (SRF) expression and position weight matrix (PWM) scan data resulted in 64 predicted SRF gene targets. This tool recognizes that SRF is the central component of a hierarchical cascade model of muscle-specific gene transcriptional network. It is able to select genes with balanced fold changes between binding and expression data.

sMBPLS / sparse Multi-Block Partial Least Squares

Identifies multi-dimensional regulatory modules in diverse types of genomic data. sMBPLS can detect combinations of multiple types of genomic markers that jointly impact the expression of a set of genes. This method can be applied to a suite of genomic profiles from ovarian cancer samples, including copy number variation (CNV), DNA methylation (DM), microRNA and gene expression (GE) data. This tool is able to extract coherent substructures from large-scale, complex datasets, and facilitating downstream biological analysis.

ARACNE / Algorithm for the Reconstruction of Accurate Cellular Networks

An algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by co-expression methods. ARACNE shows promise in identifying direct transcriptional interactions in mammalian cellular networks, a problem that has challenged existing reverse engineering algorithms. This approach should enhance our ability to use microarray data to elucidate functional mechanisms that underlie cellular processes and to identify molecular targets of pharmacological compounds in mammalian cellular networks.

BANFF / BAyesian Network Feature Finder

An R package for gene network feature selection. BANFF provides a package of posterior inference, model comparison, and graphical illustration of model fitting. The model was extended to a more general form, and a parallel computing algorithm for the Markov chain Monte Carlo (MCMC)-based posterior inference and an Expectation Maximization (EM)-based algorithm for posterior approximation were added. Based on simulation studies, we demonstrate the use of BANFF on analyzing gene expression on a protein-protein interaction network.


Offers an easy access to both Gene Regulatory Networks (GRNs) and miRNAs to the end user. SpidermiR integrates co-expression, physical interaction, co-localization, genetic influence, pathways, and shared protein domains with differentially expressed genes obtained from The Cancer Genome Atlas (TCGA). It allows the miRNAs with GRNs integration in order to obtain miRNA–gene–gene and miRNA–protein–protein interactions, and to analyze miRNA GRNs in order to identify miRNA–gene communities.

EGAD / Extending Guilt-by-Association by Degree

Predicts members of gene groups, assesses how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. Two of the core features of EGAD are: a function prediction algorithm, allowing network characterization across thousands of functional groups to be accomplished in minutes in cross-validation, and an analytic determination of the optimal prediction across multiple functional sets. EGAD has been designed to introduce ever more ‘sophisticated’ gene network algorithms, in hopes of exploiting purportedly more complex patterns.

ARACNe-AP / Algorithm for the Reconstruction of Accurate Cellular Networks with Adaptive Partitioning

ARACNe represents one of the most widely used reverse engineering algorithms by the scientific community and has been extensively experimentally validated. ARACNe uses an information theoretic framework, based on the data processing inequality theorem, to infer direct regulatory relationships between transcriptional regulator proteins and target genes. ARACNe-AP is a completely new implementation of ARACNe, based on an adaptive partitioning strategy (AP) for estimating the mutual information. ARACNe-AP achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the mutual information estimator and the network inference accuracy of the original algorithm.


Applies convolutional neural networks (CNNs) to learn the functional activity of DNA sequences from genomics data. Basset is a package to apply deep CNNs to learn DNA sequence activity. Basset effectively learned the complex code of DNA accessibility across many cell types and substantially surpassed the predictive accuracy of the present state of the art. We demonstrated how our model precisely implicates the nucleotides driving activity, highlighting genomic positions with either fragile activity that can be lost by mutation or latent potential activity that can be unlocked by mutation.


An algorithm for the inference of gene regulatory networks from expression data. It decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link.

LDGM / Latent Differential Graphical Model

A method to infer differential network among different tissues. LDGM method allows to estimate the differential network between two tissue types directly, without inferring the network for individual tissues and without assuming normal distribution of the gene expression values. This approach also has a clear advantage of utilizing much smaller sample size to achieve reliable differential network estimation. The LDGM method may provide a unique way of integrating network inference from large gene expression data sets such as GTEx and regulatory genomics data sets from ENCODE and Roadmap Epigenomics projects to better ascertain the GRN dynamics globally across different tissue types and cell types.

MIPRIP / Mixed Integer linear Programming based Regulatory Interaction Predictor

Predicts regulators of a gene of interest from gene expression profiles of the samples under study and known regulator binding information (from e.g. ChIP-seq/ChIP-chip databases). MIPRIP is developed to study the specific regulation of the gene of interest in one group of samples compared to a control group. MIPRIP can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. two different phenotypes, disease/healthy controls or treatment/controls.


Enables estimation and identification of gene regulatory networks observed indirectly through noisy measurements based on various expression technologies. BoolFilter implements the Partially-Observed Boolean Dynamical System (POBDS) model and associated algorithms. The software allows estimation of the Boolean states, network topology, and noise parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulation of the transcriptomic data for a given Boolean network model.

HTS-Net / High-Throughput screening - Network analysis

Provides a method for RNAi screenings network analyses. HTS-Net intends to improve the Interactome-Transcriptome Integration (ITI) algorithm. The pipeline aims to optimize the subnetworks detection by investigating the whole seed’s neighboring nodes and integrate regulation data into the framework. Subsequently, the method replaces hits into their biological context including information about their physical interactors and their regulators.


Infers regulators for each target gene purely from a gene expression matrix. GRNBoost uses gradient boosted stumps as the base learner. It will permit network inference on very large data sets. The tool was applied on 3K, 10K, and 100K cells from the embryonic mouse brain. It was able to infer gene regulatory networks within 18 and 37 minutes, and 8 hours respectively. GRNBoost constructs a predictor matrix that contains the expression values for all candidate regulator genes.


Searches for possible co-regulatory loops between regulon pairs generated by the RTN (Reconstruction of Transcriptional Networks) package. RTNduals searches for targets shared between pairs of regulators, using regulatory networks generated by the RTN package. The tool infers “dual regulon” status, a new concept that tests whether pairs of regulators have similar effects on their sets of target genes (regulons). Also it can identify regulators with shared or opposing effects on cellular phenotypes and can be applied to many different regulatory processes.

APA / Altered Pathway Analyzer

Identifies and prioritizes altered pathways, including those which are differentially regulated by transcription factors (TFs), by quantifying rewired sub-network topology. Altered Pathway Analyzer (APA) helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. The tool is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets.

MBTPROM / Mycobacterium tuberculosis Probabilistic Regulation of Metabolism

Encapsulates a substantially expanded knowledge base of underlying metabolic and regulatory mechanisms. MTBPROM2.0 is a model that can predict metabolic consequences of transcription factor (TF) knockout or overexpression under different environmental conditions, and suggest hypotheses of underlying molecular mechanisms that contribute to consequent phenotypes. It could be used to help to improve overall predictive accuracy for regulatory-metabolic models.


An R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, COREGNET can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool.

FGNet / Functional Gene Networks

An R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use.

SMITE / Significance-based Modules Integrating the Transcriptome and Epigenome

Integrates transcriptional and epigenetic regulatory data without loss of resolution. SMITE combines p-values by accounting for the correlation between non-independent values within data sets, allowing genes and gene modules in an interaction network to be assigned significance values. Applying to a complex genomic data set including the epigenomic and transcriptomic effects of Toxoplasma gondii infection on human host cells, the software is able to identify novel subnetworks of dysregulated genes. It allows integration of transcriptional and epigenetic regulatory data from genome-wide assays to boost confidence in finding gene modules reflecting altered cellular states.

CABERNET / Cytoscape app for the generation and the Analysis of Boolean models of gene Regulatory NETworks

A Cytoscape app for the generation, simulation and analysis of Boolean models of gene regulatory networks, specifically focused on their augmentation when an only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABERNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABERNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist.

ENCODE ChIP-Seq Significance Tool

Offers a way to conduct comparative analyses from a list of gene/transcript signatures. ENCODE ChIP-Seq Significance Tool identifies enriched transcription factors (TFs) in gene or transcript lists and presents the separate results from each list in a unified view. It recognizes the number of genes in each gene list by using the database associated with the tool. The software can be used for microarray data and other data not generated from genome- or transcriptome-wide assays.