Showing 5 open source projects for "deep"

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  • 1

    ExCon Imager

    Gigapixel GPU accelerated radio interferometric imager

    ExCon can create images with a very wide field of view (more than billion pixels). It implements novel gridding and image weighting algorithms. It is well suited to make ultra deep radio interferometric images, minimizing aliasing artefacts and PSF variation. NVIDIA GPU acceleration is available. JAVA/SCALA support available.
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  • 2
    MELAGE
    ...It allows to load two, three and four-dimensional images of both techniques and in the case of 3D images it allows the simultaneous visualization of the three orthogonal planes which facilitates the localization of the regions of interest. It has been developed in Python with a user-friendly interface for healthcare personnel. Thanks to Artificial Intelligence and deep learning methods, MELAGE has tools to estimate volumes of different regions of interest in both images. Moreover, it allows to perform linear, area and volumetric measurements in a very intuitive and easy way, being able to instantly see the segmented region in a new tab. Please see https://melage.uca.es/
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  • 3
    OmicSelector

    OmicSelector

    Feature selection and deep learning modeling for omic biomarker study

    OmicSelector is an environment, Docker-based web application, and R package for biomarker signature selection (feature selection) from high-throughput experiments and others. It was initially developed for miRNA-seq (small RNA, smRNA-seq; hence the name was miRNAselector), RNA-seq and qPCR, but can be applied for every problem where numeric features should be selected to counteract overfitting of the models. Using our tool, you can choose features, like miRNAs, with the most significant...
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  • 4
    sRNAWorkbench

    sRNAWorkbench

    The UEA sRNA Workbench

    A suite of tools for analysing small RNA (sRNA) data from Next Generation Sequencing devices. Including expression profiling of known mirco RNA (miRNA), identification of novel miRNA in deep-sequencing data and identification of other interesting landmarks within high-throughput genetic data
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  • 5
    This application allow user to predict dissolution profile of solid dispersion systems based on algorithms like symbolic regression, deep neural networks, random forests or generalized boosted models. Those techniques can be combined to create expert system. Application was created as a part of project K/DSC/004290 subsidy for young researchers from Polish Ministry of Higher Education.
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