Pathway crosstalk network prediction software tools | Protein interaction data analysis
Cells communicate with their environment via signal transduction pathways. On occasion, the activation of one pathway can produce an effect downstream of another pathway, a phenomenon known as crosstalk.
A webservice for pathway annotation based on crosstalk derived through FunCoup, a framework for genome wide functional association networks. PathwAX runs the BinoX algorithm, which employs Monte-Carlo sampling of randomized networks and estimates a binomial distribution, for estimating the statistical significance of the crosstalk. A pathway is statistically enriched/depleted if the crosstalk, which is the number of links between the pathway and your gene set, is more/less than one would observe in a random network. This results in substantially higher accuracy than gene overlap methods.
A genome wide network analysis tool. BinoX determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. It may be employed for any type of gene set analysis as long as a comprehensive functional association network exists for the genes. BinoX offers superior performance compared to existing methods in terms of true positive and false positive rates (FPRs).
Constructs clusters greedily, starting from local seeds that have high weighted degree, and adding nodes that maintain the density of the clusters. SPICi is based on a simpler cluster expansion approach, employs a different seed selection criterion and incorporates interaction confidences. It shows good performance in recapitulating protein complexes, deteriorating only on extremely incomplete networks. This tool is robust to perturbations in dense functional networks.
Discovers disease related protein complexes. Disease complex integrates network propagation with an integer program algorithm designed to discover dense clusters with highly specific interactions. The computational framework works in two conceptual phases: (i) identification of network regions potentially associated with the disease under study; and (ii) inference of densely interacting protein clusters within those regions. Disease complex was designed to address the problem of protein complex detection by devising a framework that integrates network propagation with a novel integer program algorithm designed to discover dense clusters with highly specific interactions.
A path-based approach for identifying pairs of pathways that may crosstalk. XTalk computes the statistical significance of the average length of multiple short paths that connect receptors in one pathway to the transcription factors in another. By design, XTalk reports the precise interactions and mechanisms that support the identified crosstalk.
Assists in navigating large interaction networks by linking two nodes or two groups of nodes with each other. viPEr is a Cytoscape plugin that integrates omics data with interactome data. It can be used to identify potential links between processes or pathways. It also enables users to explore the neighborhood of a single node with respect to the numerical quality of radiating paths.
A statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA).
Furnishes a method for reconstructing the topology of a signaling network. SiNeC performs in three steps: (1) it estimates the approximate ordering of the critical genes in the reference network; (2) it removes edges that are in conflict with an order from the reference network; and (3) it inserts the missing edges that are necessary to ensure the flow between consecutive critical genes and the consistency of the remaining genes in the reference network.
Enables large-scale signaling network reconstruction. S-SiNeC can construct networks involving hundreds of proteins with minimum sacrifice in optimality. This method has polynomial time complexity, but may fail to return a network that satisfies all the constraints enforced by the RNA interference (RNAi) data. It can be useful for biologists to construct novel signaling networks from in vivo or in vitro screening experiments.
Trains and evaluates knowledge graph embeddings (KGEs) on biological knowledge graphs (KGs). BioKEEN consists of three layers: (1) the model configuration layer, (2) the data acquisition and transformation layer, and (3) the learning layer. The software enables users without expert knowledge in machine learning to learn and apply biological based KGE. It was tested on several KGE models on the pathway mappings from ComPath.
Explores interactions between tumor epithelial and stromal cells in a bipartite manner. CrosstalkNet can be used to efficiently visualize, mine, and interpret large co-expression networks. It has multiple utilities that allow for exploring different levels of neighbours, determining if there are paths between two genes of interest, and finding out what genes are highly connected. CrosstalkNet assists biologists and clinicians in exploring large interaction graphs to obtain insights into the biological processes that govern the tumor epithelial-stromal crosstalk. This tool is available online and its source code is freely available.
Performs training and evaluation of knowledge graph embeddings (KGEs). PyKEEN is a Python package that consists of BioKEEN's core component for training and evaluating KGE models. This program has a modular architecture and can be configured users.
Identifies a user-specified number of orthogonal communication channels from a library of characterized small molecule acylserine homolactones (AHL) -receiver devices. This software computes the specific control of gene expression through three non-interfering AHL communication channels in a polyclonal E. coli co-culture. It estimates results of GraphPad Prism (GFP) output of AHL-receiver devices with fitted model input/output curves.
Assists users in understanding association and interrelation of age-related disorders (ARDs) and associated proteins, pathways, and drugs. ARDnet allows construction of networks of ARDs associated proteins, drugs, and pathways and provides a methodology for analyzing and visualizing ARDs related data. This tool incorporates several age-related disorders and their associated proteins information as well as information about drugs and their ARDs protein targets.
Generates complementary networks of pathways linked to a specific biological status to relate pathways together. PathwayConnector permits users to provide: (1) direct connection between pathways of interest; (2) complementary networks that demonstrate the shortest paths between pathways of interest and intermediate pathways; and (3) additional clustering methods to show communities of pathways.
Identifies latent dysregulated pathways by considering the global influence of both within-pathway effects and crosstalk between pathways. Input of expression profiles with two biological states can produce information on dysregulated pathways within a few minutes. PAGI initially uses t-test statistics to evaluate the extent of differential expression for each gene, and for all genes represented in the expression profiles were mapped to a global gene-gene network reflecting the relationships both within and between pathways. It then uses the random walk with restart algorithm to calculate the global dysregulated score of each gene, representing the extent to which genes are affected by global influence from both the internal effect of pathways and crosstalk between pathways. Finally, it uses cumulative distribution functions to evaluate each pathway and the pathways are prioritized by false discovery rate (FDR).
A method for computing disease similarity by integrating medical literature and protein interaction network. MedNetSim consists of a network-based method (NetSim), which employs the entire protein interaction network, and a MEDLINE-based method (MedSim), which computes disease similarity by mining the biomedical literature. MedNetSim, MedSim and NetSim are freely available online. The user can enter two diseases of interest; the web service will compute their similarity and present a corresponding p-value.
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