ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) is a novel 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 vast majority of indirect interactions typically inferred by pairwise analysis.
B Cell Interactome
The B cell interactome (BCI) is a network of protein-protein, protein-DNA and modulatory interactions in human B cells. The network contains known interactions (reported in public databases) and predicted interactions by a Bayesian evidence integration framework which integrates a variety of generic and context specific experimental clues about protein-protein and protein-DNA interactions - such as a large collection of B cell expression profiles - with inferences from different reverse engineering algorithms, such as GeneWays and ARACNE. Modulatory interactions are predicted by the MINDY (please, refer to the publication section for more information).
Two R scripts for removing location biases from a multiwell dataset.
R-system software package for the assembly of informative, transcript-specific probe-clusters for Affymetrix expression microarrays.
R-system package implementation of the DeMAND (Detecting Mechanism of Action based Network Dysregulation) algorithm.
A Bioconductor package for identifying genetic variants that lie upstream of master regulators and drive cellular phenotypes.
geWorkbench is a Java-based open-source platform for integrated genomics. Using a component architecture it allows individually developed plug-ins to be configured into complex bioinformatic applications. At present there are more than 30 available plug-ins supporting the visualization and analysis of gene expression and sequence data.
MATLAB functions for prediction of ceRNA (competing endogenous RNA) interactions from expression profiles of candidate RNAs and their common miRNA regulators using conditional mutual information.
The Master Regulator Inference Algorithm identifies transcription factors (TFs) that control the transition between the two phenotypes, A and B, and the maintenance of the latter phenotype.
An algorithm for the genome-wide discovery of modulators of transcriptional interactions), a new information theoretic method to identify multivariate statistical dependencies between a transcription factor and one or more of its targets, conditional on the presence (or absence) of a candidate modulator gene.
R-system package including the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) and the MAster Regulator INference Analysis (MARINa) algorithms.