{ "info": { "author": "Shani Cohen", "author_email": "shani.cohen.33@gmail.com", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: Microsoft :: Windows", "Operating System :: POSIX :: Linux", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.6" ], "description": "minMLST is a machine-learning based methodology for identifying a minimal subset of genes that preserves high discrimination among bacterial strains. It combines well known machine-learning algorithms and approaches such as XGBoost, distance-based hierarchical clustering, and SHAP. \nminMLST quantifies the importance level of each gene in an MLST scheme and allows the user to investigate the trade-off between minimizing the number of genes in the scheme vs preserving a high resolution among strain types.\n\n See more information in [GitHub](https://github.com/shanicohen33/minMLST).\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/shanicohen33/minMLST", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "minmlst", "package_url": "https://pypi.org/project/minmlst/", "platform": "", "project_url": "https://pypi.org/project/minmlst/", "project_urls": { "Homepage": "https://github.com/shanicohen33/minMLST" }, "release_url": "https://pypi.org/project/minmlst/0.1.0/", "requires_dist": [ "shap (>=0.28.5)", "xgboost (>=0.82)", "dill (>=0.3.0)" ], "requires_python": "", "summary": "Machine-learning based minimal MLST scheme for bacterial strain typing", "version": "0.1.0" }, "last_serial": 5865898, "releases": { "0.1.0": [ { "comment_text": "", "digests": { "md5": "520ac62edaf448ee84602d398e6041a4", "sha256": "8790d7be4ac214558150f2a99f32701a60de8d171b40bcdac792db9ee82bd3a6" }, "downloads": -1, "filename": "minmlst-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "520ac62edaf448ee84602d398e6041a4", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 13545, "upload_time": "2019-09-21T12:23:47", "url": "https://files.pythonhosted.org/packages/b6/5a/c8714f67829c58e266581fcf3f73add9aba57bc68f6629523b27fb835da0/minmlst-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "b7d6d4e49fb8ad800446af1ddc066872", "sha256": "3a2f000a9fb106908809b497019f8a6de9d3bda77114f83cf9777cadd2e0b1f3" }, "downloads": -1, "filename": "minmlst-0.1.0.tar.gz", "has_sig": false, "md5_digest": "b7d6d4e49fb8ad800446af1ddc066872", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 12358, "upload_time": "2019-09-21T12:23:49", "url": "https://files.pythonhosted.org/packages/30/c4/39bec511e5ca88daa4d64c954f15323e0558a223a69d0aa93be9f75598c1/minmlst-0.1.0.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "520ac62edaf448ee84602d398e6041a4", "sha256": "8790d7be4ac214558150f2a99f32701a60de8d171b40bcdac792db9ee82bd3a6" }, "downloads": -1, "filename": "minmlst-0.1.0-py3-none-any.whl", "has_sig": false, "md5_digest": "520ac62edaf448ee84602d398e6041a4", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 13545, "upload_time": "2019-09-21T12:23:47", "url": "https://files.pythonhosted.org/packages/b6/5a/c8714f67829c58e266581fcf3f73add9aba57bc68f6629523b27fb835da0/minmlst-0.1.0-py3-none-any.whl" }, { "comment_text": "", "digests": { "md5": "b7d6d4e49fb8ad800446af1ddc066872", "sha256": "3a2f000a9fb106908809b497019f8a6de9d3bda77114f83cf9777cadd2e0b1f3" }, "downloads": -1, "filename": "minmlst-0.1.0.tar.gz", "has_sig": false, "md5_digest": "b7d6d4e49fb8ad800446af1ddc066872", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 12358, "upload_time": "2019-09-21T12:23:49", "url": "https://files.pythonhosted.org/packages/30/c4/39bec511e5ca88daa4d64c954f15323e0558a223a69d0aa93be9f75598c1/minmlst-0.1.0.tar.gz" } ] }