Showing 5 open source projects for "network analysis"

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  • 1

    Stanford Data Miner

    Tools for integration and analysis of heterogeneous immunological data

    An extensive description of this system is published in the Journal of Translational Medicine (http://www.translational-medicine.com/). In brief, the system consists of two main web applications, a data integration app and a data exploration app. The data integration app is a fully custom Java "Web 2.0" product called Sherpa. Sherpa uses Seam, a platform integrating Asynchronous JavaScript and XML (AJAX), JavaServer Faces (JSF), the Java Persistence API (JPA), and Enterprise Java Beans...
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  • 2
    Grinn

    Grinn

    graph database and R package for omic data integration

    http://kwanjeeraw.github.io/grinn/
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  • 3
    optPBN

    optPBN

    An optimization toolbox for probabilistic Boolean networks

    We introduce optPBN, a Matlab-based toolbox for the optimization of probabilistic Boolean networks (PBN) which operates under the framework of the BN/PBN toolbox from Shmulevich et al. optPBN offers an easy generation of probabilistic Boolean networks from Boolean rule-based modeling and allows for flexible measurement data integration from multiple experiments and a subsequent integrated optimization problem generation which then can be solved with different optimizers. Thereby optPBN...
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  • 4
    Open Information Integration
    Open Information Integration Tool Suite (Open II) is used by analysts and programmers to accelerate data integration and harmonization across organizations. OpenII has a neutral schema repository for browsing and comparing all sorts of data models. OpenII is built as a Rich Client Platform Application on top of Eclipse 3.x. Developers need to download Eclipse, install the RCP support, the Fatjar plugin and the Delta Pack in one of the 3.x flavors. Release Notes Release Date: Jan...
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  • 5
    The Stem Cell Artificial Neural network project entails the analysis and integration of genomics data for extracting the stemness signature of several tissues by training a multiclass single-layer linear artificial neural network.
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