moiety-modeling 1.0.3.1

Moiety Modeling Implementation

moiety_modeling package provides facilities for moiety model representation, model optimization and model selection.

Citation

Please cite the GitHub repository until our manuscript is accepted for publication: https://github.com/MoseleyBioinformaticsLab/moiety_modeling.git

Installation

‘moiety_modeling’ runs under Python 3.6+ and is available through python3-pip. Install via pip or clone the git repo and install the following depencies and you are ready to go!

Install on Linux

Pip installation

python3 -m pip install moiety-modeling

GitHub Package installation

Make sure you have git installed:

git clone https://github.com/MoseleyBioinformaticsLab/moiety_modeling.git

Dependencies

‘moiety_modeling’ requires the following Python libraries:

• docopt for creating the command-line interface.

• jsonpickle for saving Python objects in a JSON serializable form and outputting to a file.

• numpy and matplotlib for visualization of optimized results.

• scipy for application of optimization methods.

• SAGA-optimize for parameters optimization.

Quickstart

Using moiety_modeling to optimize parameters of moiety model.

python3 -m moiety_modeling modeling --models=<model_jsonfile> --datasets=<dataset_jsonfile> --optimizations=<optimizationSetting_json> --repetition=100 --split --multiprocess --energyFunction=logDifference

Using moiety_modeling to analyze optimized results and select the optimal model.

python3 -m moiety_modeling analyze optimizations --a <optimizationPaths_txtfile>
python3 -m moiety_modeling analyze rank <analysisPaths_txtfile> --rankCriteria=AICc

Using moiety_modeling to visualize the optimzed results.

python3 -m moiety_modeling plot moiety <analysisResults_jsonfile>

Note

Read the User Guide and the moiety_modeling Tutorial on ReadTheDocs to learn more and to see code examples on using the moiety_modeling as a library and as a command-line tool.

License

Made available under the terms of The modified Clear BSD License. See full license in LICENSE.

Authors

• Huan Jin

• Hunter N.B. Moseley