De novo network enrichment software tools | Protein interaction data analysis
De novo network enrichment approaches have become increasingly popular. Although existing approaches differ in many relevant aspects (optimization criteria, algorithmic implementation, scoring function, etc.), they all aim for extracting connected subnetworks from a larger interaction network. These are significantly enriched with active, i.e. deregulated, biological entities (genes, proteins, metabolites).
Provides functions for the import-export of some standard systems biology file formats and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM provides the user with a complete interface for the analysis of biological networks in Cytoscape environment.
A comprehensive R-package for the analysis of biological networks including an exact and a heuristic approach to identify functional modules. The BioNet package provides an extensive framework for integrated network analysis in R. This includes the statistics for the integration of transcriptomic and functional data with biological networks, the scoring of nodes as well as methods for network search and visualization.
Finds clusters where member nodes show significant changes in expression levels. jActiveModules is a plugin that searches a molecular interaction network to find expression activated subnetworks. Such subnetworks are connected regions of a network that show significant changes in expression over particular subsets of conditions. The method combines a rigorous statistical measure for scoring subnetworks with a search algorithm for finding subnetworks with high score.
Finds high weight subnetworks in a vertex-weighted network. HotNet can recognize significantly mutated groups of interacting genes from large cancer sequencing studies. It is based on an “insulated” heat diffusion process that simultaneously analyzes a gene’s mutations and its local topology. This tool can deal with scores on individual genes/proteins as well as the topology of interactions between genes/ proteins.
A program for detection of functional modules using interaction networks and expression data. MATISSE implements several algorithmic engines that analyze together expression and network data. Each of the algorithms addresses a different research question and scenario.
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