Search Results for "python feature selection"

Showing 5 open source projects for "python feature selection"

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  • 1

    fscaret_shiny

    UI for fscaret

    User Interface (ui) application which implements the automated feature selection provided by the 'fscaret' package of R-environment.
    Downloads: 0 This Week
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  • 2

    NGSbinningpytools

    Python tools for binning NGS scaffolds to various genomes.

    Next generation sequencing has opened new avenues for sequencing prokaryotes which were difficult or either impossible to culture. Now, it is possible to sequence all organisms present in a metagenomic community. However, a critical problem is to determine the host organism for each genomic fragment present in the dataset. This project provides the computational tools for assigning the organismal identity for each genomic scaffold in the given dataset. For more detailed documentation...
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  • 3
    GEPETTO - Gene Prioritization in Java

    GEPETTO - Gene Prioritization in Java

    GEPETTO (GEne Prioritization ExTended TOol)

    ...GEPETTO is written in Java/Python and supported by an advanced modular architecture, which means that it can easily be modified and extended by the user, in order to include alternative scoring methods and new data sources. We intend to extend the system from gene-level to variant-level prioritization, by exploiting the variant data in the MSV3D database. Contact: bmhoan@gmail.com or walter.vincent.fr@gmail.com
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  • 4
    imDEV

    imDEV

    Tools for multivariate data visualization, exploration and analysis.

    You may also like some newer work: http://createdatasol.com/ Project imDEV is an application of RExcel, which seamlessly integrates Excel and R for tasks focused on multivariate data visualization, exploration, and analysis. Interactive modules for dimensional reduction (imPCA), prediction (imPLS), feature selection, analysis of correlation (imCorrelations) and generation of networked structures (imGraph) provide an integrated environment for systems level analysis of multivariate data.
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  • 5
    Tools to build molecular-docking activity prediction models by PLS regression with iterative training and pose-selection. Descriptors include (i) docking score(s), (ii) pharmacophore features, (iii) multi-feature descriptors learned by decision trees.
    Downloads: 0 This Week
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