Employs a scoring function that jointly measures the condition-specific changes of both 'nodes' (individual genes) and 'edges' (gene-gene co-expression). COSINE uses the genetic algorithm to search for the single optimal sub-network which maximizes the scoring function. Compared with previous methods, COSINE is more powerful in identifying truly significant sub-networks of appropriate size and meaningful biological relevance.
Provides a suite of features to assist biologists in performing pathway- and network-based data analysis in a biologically intuitive and user-friendly way. Biologists can use ReactomeFIViz to uncover network and pathway patterns related to their studies, search for gene signatures from gene expression data sets, reveal pathways significantly enriched by genes in a list, and integrate multiple genomic data types into a pathway context using probabilistic graphical models.
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.
Analyzes gene expression data using gene interaction networks. GXNA can be used for the analysis of virtually any microarray data set. The software incorporates a broad definition of a network, including both protein–protein and DNA–protein interactions. All interactions are treated equally, regardless of type and direction. It was tested on three human microarray data sets, related to cancer and the immune system.
A network-based software package developed to explore the functional relevance of genes generated as an intermediate result from numerous high-throughput technologies. GenRev searches for optimal intermediate nodes (genes) for the connection of input nodes via several algorithms, including the Klein-Ravi algorithm, the limited kWalks algorithm and a heuristic local search algorithm. Gene ranking and graph clustering analyses are integrated into the package. GenRev has the following features. (1) It provides users with great flexibility to define their own networks. (2) Users are allowed to define each gene's importance in a subnetwork search by setting its score. (3) It is standalone and platform independent. (4) It provides an optimization in subnetwork search, which dramatically reduces the running time. GenRev is particularly designed for general use so that users have the flexibility to choose a reference network and define the score of genes.
A pathway analysis methodology. MinePath facilitates the decomposition of pathways into their constituent sub-paths. Decomposition leads to the transformation of single-relations to complex regulation subpaths. MinePath assess the significance of the pathways as a whole, ranking them by their p-values. It offers dynamic and rich pathway visualization functionality, with the unique characteristic to color regulatory relations between genes and reveal their phenotype inclination.
Detects differentially expressed subpathways. Pathome has two specificities: it analyzes the regulation information between nodes in the biological pathways and can be applied to a small number of samples. It does not need more samples to obtain a null distribution for a statistical test. This software is appropriate to use for the biological interpretation of patterns from gene expression data.