Showing 6 open source projects for "python module"

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  • 1

    BioC

    We describe a simple XML format to share text documents and annotation

    A minimalist approach to share text documents and data annotations. Allows a large number of different annotations to be represented. Project files contain: - simple code to hold/read/write data and perform sample processing. - BioC-formatted corpora - BioC tools that work with BioC corpora BioC goals - simplicity - interoperability - broad use - reuse There should be little investment required to learn to use a format or a software module to process that format. We...
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    Downloads: 13 This Week
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  • 2

    PRADA

    PRADA : Pipeline for RNA-Sequencing Data Analysis

    Massively parallel sequencing of cDNA reverse transcribed from RNA (RNASeq) provides an accurate estimate of the quantity and composition of mRNAs. To characterize the transcriptome through the analysis of RNA-seq data, we developed PRADA. PRADA focuses on the processing and analysis of gene expression estimates, supervised and unsupervised gene fusion identification, and supervised intragenic deletion identification. PRADA currently supports 7 modules to process and identify...
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  • 3

    AMBIENT

    Find active modules in metabolic networks using high-throughput data

    IMPORTANT: Since publication of the AMBIENT method in BMC Sys Bio, several updates have been made. If you wish to use the version used in the paper it is v0.6.3, however I recommend using the latest version which works in the same way but with additional options and has stability and performance improvements. Thanks for your interest! AMBIENT (Active Modules for Bipartite Networks) is a Python module that uses simulated annealing to find areas of a metabolic network (modules) that have...
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  • 4
    Python module for manipulation of Crystallographic Information Framework (CIF) files
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    NeuroPython is a Python module created for easy simulation of neural networks.
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  • 6
    ABE is a small, fast and convenient program for visualizing and modeling experimental bioassay data. The data can be modeled using either polynomials or a more specific four-parameter model based upon the standard, sigmoidal dose-response curve.
    Downloads: 0 This Week
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