{ "info": { "author": "Stefan Stojanovic", "author_email": "stefs304@gmail.com", "bugtrack_url": null, "classifiers": [ "Intended Audience :: Healthcare Industry", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Bio-Informatics", "Topic :: Scientific/Engineering :: Chemistry" ], "description": "# MHCLovac\nMHC binding prediction based on modeled physicochemical properties of peptides.\n\n*__New release - MHCLovac 2.0__*\n\n*__MHCLovac 2.0 makes almost somewhat accurate predictions. \nIt really is not precise.__*\n\n* [About](#About)\n* [Installation](#Installation)\n* [Usage](#Usage)\n\n
\n\n### About \n\nMHCLovac uses Bayesian linear regression for binding affinity prediction\nbased on modeled physicochemical properties of peptides.\nMHCLovac uses pre-developed [**`proteinko`**](https://github.com/stefs304/proteinko) \npackage to obtain modeled distributions of physicochemical properties. \n\n![modeled_distributions](https://raw.githubusercontent.com/stefs304/proteinko/dev/resources/plot1.png)\n\nPhysicochemical properties in question are:\n* Hydropathy\n* Number of donor hydrogen bonds\n* Number of acceptor hydrogen bonds\n* Isoelectric point\n* Van der Waals molecular volume\n\n Once the distributions are obtained, the area under the curve (AUC) is \n calculated using a sliding frame technique. The AUC values for each of five\n physicochemical properties are concatenated into single feature vector. \n\nModel training is performed on standardized AUC values. We tested number of \nlinear regression models and concluded that BayesianRidge algorithm from `sklearn`\npackage produces most consistent predictions across various training set \nconfigurations.\n\n![regression_models](https://raw.githubusercontent.com/stefs304/mhclovac/dev/resources/plots/REGRESSION-MODELS.2019-04-27T21%3A12%3A47.348099.png)\n\nMHCLovac makes modestly accurate predictions, which can be seen on plots below.\n\n![binding_predictions](https://raw.githubusercontent.com/stefs304/mhclovac/dev/resources/plots/BINDING_PREDICTIONS.2019-05-01T13%3A31%3A05.698974.png)\n\n\n### Installation\n\nInstall from PyPI repository\n```\npip install mhclovac\n```\n\nDownload and install from git repository\n```\ngit clone https://github.com/stefs304/mhclovac\ncd mhclovac\npip install .\n```\n\n### Usage\n```\nmhclovac --fasta \n --hla \n --peptide_length \n --output \n```\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": 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