Metadata-Version: 2.1
Name: emmaa
Version: 1.17.0
Summary: Ecosystem of Machine-maintained Models with Automated Analysis
Home-page: https://github.com/indralab/emmaa
Author: EMMAA developers, Harvard Medical School
Author-email: benjamin_gyori@hms.harvard.edu
License: UNKNOWN
Description: # EMMAA
        EMMAA is an Ecosystem of Machine-maintained Models with Automated Analysis.
        The primary way users can interact with EMMAA is by using the EMMAA Dashboard
        which can be accessed 
        [here](http://emmaa.indra.bio).
        
        ## Documentation
        For a detailed documentation of EMMA, visit http://emmaa.readthedocs.io.
        The documentation contains three main sections:
        - A conceptual description of the [EMMAA architecture and approach](https://emmaa.readthedocs.io/en/latest/architecture/index.html)
        - An [introduction to the EMMAA Dashboard](https://emmaa.readthedocs.io/en/latest/dashboard/index.html)
        - A [module-level documentation of all of EMMAA's code base](https://emmaa.readthedocs.io/en/latest/modules/index.html) linked directly to the source code on Github
        
        ## Concept
        The main idea behind EMMAA is to create a set of computational models that
        are kept up-to-date using automated machine reading, knowledge-assembly, and
        model generation. Each model starts with a prior network of relevant concepts
        connected through a set of known mechanisms. This set of mechanisms is then
        extended by reading literature or other sources of information each day,
        determining how new information relates to the existing model, and then
        updating the model with the new information.
        
        Models are also available for automated analysis in which relevant queries
        that fall within the scope of each model can be automatically mapped
        to structural and dynamical analysis procedures on the model. This allows
        recognizing and reporting changes to the model that result in meaningful
        changes to analysis results.
        
        ## Applications
        The primary application area of EMMAA is the molecular biology of cancer,
        however, it can be applied to other domains that the INDRA system and the
        reading systems integrated with INDRA can handle.
        
        ## Installation
        Users primarily interact with EMMAA via the
        [Dashboard](http://emmaa.indra.bio), for which no dependencies need to be
        installed.
        
        To set up programmatic access to EMMAA's features locally, do the following:
        ```
        git clone https://github.com/indralab/emmaa.git
        cd emmaa
        pip install git+https://github.com/sorgerlab/indra.git
        pip install git+https://github.com/indralab/indra_db.git
        pip install -e .
        ```
        
        A Dockerized version of EMMAA is available at
        https://hub.docker.com/r/labsyspharm/emmaa, which can be obtained as
        ```
        docker pull labsyspharm/emmaa
        ```
        
        ## Funding
        The development of EMMAA is funded under the DARPA Automating Scientific
        Knowledge Extraction (ASKE) program under award HR00111990009.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
Provides-Extra: test
