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PathwAX
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.
SPICi / Speed and Performance In Clustering
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.
Disease complex
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.
CrossTalkZ
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).
CrosstalkNet
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.
PAGI
Obsolete
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).
MedNetSim
Obsolete
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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