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GIANT / Genome-scale Integrated Analysis of Networks in Tissues
An interface to human tissue networks through multi-gene queries, network visualization, analysis tools including NetWAS, and downloadable networks. GIANT enables systematic exploration of the landscape of interacting genes that shape specialized cellular functions across more than one hundred human tissues and cell types. GIANT is available through a dynamic, interactive web interface. Researchers can query by individual genes or by gene sets of interest to analyze tissue-specific gene function and interactions. In addition to the interface, all of the networks are provided for download, and the full list of input datasets and their sources is available through the webserver.
A versatile web server for interactive network analysis and visualization. StemCellNet rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer.
RESQUE / REduction-based scheme using Semi-Markov scores for network QUErying
Allows network querying. RESQUE takes a reduction-based approach, where the target network is iteratively reduced based on the so-called node correspondence scores that are computed using a semi-Markov random walk (SMRW) model. It is able to produce good results in network querying techniques, in terms of computational efficiency and querying accuracy. This tool can deal with node insertions and deletions at arbitrary locations.
CNetQ / Conditional Random Fields based Network Querying Method
Represents a method for network querying problem. CNetQ allows users to find the conserved functionally similar modules and pathways in both undirected and directed biomolecular networks. This method permits users to: (1) realize unlimited insertions/deletions, (2) do efficiently and accurately query networks with cycles, (3) address the directed network querying problem and the undirected network querying problem, and more.
SEQUOIA / Significance Enhanced QUerying Of InterAction networks
Identifies conserved subnetwork regions in the target network that are similar to a given query network. SEQUOIA is a network querying algorithm that compares two networks and estimates the node correspondence scores by using the context-sensitive random walk model. The software can enhance the biological significance of the network querying results by estimating the node correspondence based on the context-sensitive random walk (CSRW) model and minimizing the conductance of matching network modules.
MaWISh / Maximum Weight Induced Subgraph
A comprehensive alignment tool for PPI networks, which is inspired by duplication/divergence models that focus on understanding the evolution of protein interactions. MaWISh is based on a mathematical model that extends the concepts of match, mismatch, and gap in sequence alignment to that of match, mismatch, and duplication in network alignment and evaluates the similarity between graph structures through a scoring function that accounts for evolutionary events. By relying on evolutionary models, it facilitates interpretation of resulting alignments in terms of not only conservation but also divergence of modularity in PPI networks. Furthermore, as in the case of sequence alignment, MaWISh allows flexibility in adjusting parameters to quantify underlying evolutionary relationships.
MULE / Mining Uniquely Labeled Edgesets
Detects conserved interaction patterns in biological networks. MULE uses a graph simplification technique based on ortholog contraction, which is ideally suited to biological networks, to render this problem computationally tractable and scalable to large numbers of networks. It can be used as a pruning heuristic to simplify the harder graph mining task or a closely related, but computationally simpler task, which also provides significant biological insights by identifying conserved interaction patterns among protein families.
A network alignment and search tool for comparing protein interaction networks across species to identify protein pathways and complexes that have been conserved by evolution. The basic method searches for high-scoring alignments between pairs of protein interaction paths, for which proteins of the first path are paired with putative orthologs occurring in the same order in the second path. This technique discriminates between true- and false-positive interactions and allows for functional annotation of protein interaction pathways based on similarity to the network of another, well-characterized species.
APPAGATO / APproximate PArallel and stochastic GrAph querying Tool
Finds approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. It applies to large networks and provides high performance as well as statistically significant accurate results allowed to its randomic and parallel nature. The tool can compute efficiently functional and topological node similarity together with fast searching of a large number of query matching within the target graph.
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