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        "author": "['Nicolas Renaud', 'Cunliang Geng, Li Xue']",
        "author_email": "n.renaud@esciencecenter.nl",
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            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
        ],
        "description": "# iScore\n\n**Support Vector Machine on Graph kernel for protein-protein conformation ranking**\n\n[![Build Status](https://secure.travis-ci.org/DeepRank/iScore.svg?branch=master)](https://travis-ci.org/DeepRank/iScore)\n[![Documentation Status](https://readthedocs.org/projects/iscoredoc/badge/?version=latest)](http://iscoredoc.readthedocs.io/?badge=latest)\n[![Coverage Status](https://coveralls.io/repos/github/DeepRank/iScore/badge.svg?branch=master)](https://coveralls.io/github/DeepRank/iScore?branch=master)\n[![Codacy Badge](https://api.codacy.com/project/badge/Grade/9491c221796e49c0a120ada9aed5fe42)](https://www.codacy.com/app/NicoRenaud/iScore?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=DeepRank/iScore&amp;utm_campaign=Badge_Grade)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2630567.svg)](https://doi.org/10.5281/zenodo.2630567)\n\n\n![alt text](./image/workflow.png)\n\niScore offers simple solutions to classify protein-protein interfaces using a support vector machine approach on graph kernels. The simplest way to use iScore is through dedicated binaries that hide the complexity of the approach and allows access to the code with simple command line interfaces. The two binaries are `iscore.train` and `iscore.predict` that respectively train a model using a trainging set and use this model to predict the near-native character of unkown conformations.\n\nTo use these binaries easily the following file structure is advised :\n\n```\nroot/\n |__train/\n |    |__ pdb/\n |    |__ pssm/\n |    |__ caseID.lst\n |__predict/\n      |__pdb/\n      |__pssm/\n      |__ caseID.lst (optional)\n```\n\nThe `train` subdirectory contains the PDB files and the PSSM files of the conformation contained in the training set. The PSSM files can be calculated using PSSMGen <https://github.com/DeepRank/PSSMGen>. To train the model simply go to the `train` subdirectory and type:\n\n```console\nmpiexec -n ${NPROC} iScore.train\n```\n\nThis binary will generate a archive file called by default `training_set.tar.gz` that contains all the information needed to predict binary classes of a test set using the trained model. To use this model go into the `test` subdirectory and type:\n\n```console\nmpiexec -n ${NPROC} iScore.predict --archive ../train/training_set.tar.gz\n```\n\nThis binary will output the binary class and decision value of the conformations in the test set in a text file `iScorePredict.txt`.\n\n\n\n\n\n",
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        "license": "Apache Software License 2.0",
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        "name": "iScore",
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        "summary": "Scoring protein-protein interface using RWGK and SVM",
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