moiety_modeling package provides facilities for moiety model representation, model optimization and model selection.
Please cite the GitHub repository until our manuscript is accepted for publication: https://github.com/MoseleyBioinformaticsLab/moiety_modeling.git
'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!
python3 -m pip install moiety-modeling
Make sure you have git installed:
git clone https://github.com/MoseleyBioinformaticsLab/moiety_modeling.git
'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.
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.
Made available under the terms of The modified Clear BSD License. See full license in LICENSE.
- Huan Jin
- Hunter N.B. Moseley