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Ecosystem of Machine-maintained Models with Automated Analysis

Project 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.

Documentation

For a detailed documentation of EMMA, visit http://emmaa.readthedocs.io. The documentation contains three main sections:

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, 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.

Project details


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