moiety_modeling
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-modelingGitHub Package installation
Make sure you have git installed:
git clone https://github.com/MoseleyBioinformaticsLab/moiety_modeling.gitDependencies
‘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=logDifferenceUsing 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=AICcUsing moiety_modeling to visualize the optimzed results.
python3 -m moiety_modeling plot moiety <analysisResults_jsonfile>License
Made available under the terms of The modified Clear BSD License. See full license in LICENSE.
2 maintainers
Metadata
2 maintainers
Release files for moiety-modeling 1.0.3.2
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Release files / moiety_modeling-1.0.3.2.tar.gz
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