{"id":20162905,"url":"https://github.com/vlopezferrando/pymut","last_synced_at":"2025-04-10T00:36:32.742Z","repository":{"id":62581940,"uuid":"81552433","full_name":"vlopezferrando/pymut","owner":"vlopezferrando","description":"Machine learning methods for the prediction of pathology in protein mutations, as in the PMut predictor.","archived":false,"fork":false,"pushed_at":"2018-01-08T10:27:43.000Z","size":53098,"stargazers_count":6,"open_issues_count":0,"forks_count":4,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-24T02:22:07.370Z","etag":null,"topics":["machine-learning","mutations","pathology","protein-mutations"],"latest_commit_sha":null,"homepage":"https://mmb.irbbarcelona.org/PMut/pymut","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/vlopezferrando.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2017-02-10T10:03:13.000Z","updated_at":"2020-11-30T03:37:58.000Z","dependencies_parsed_at":"2022-11-03T21:54:20.369Z","dependency_job_id":null,"html_url":"https://github.com/vlopezferrando/pymut","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vlopezferrando%2Fpymut","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vlopezferrando%2Fpymut/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vlopezferrando%2Fpymut/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/vlopezferrando%2Fpymut/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/vlopezferrando","download_url":"https://codeload.github.com/vlopezferrando/pymut/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248119296,"owners_count":21050755,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["machine-learning","mutations","pathology","protein-mutations"],"created_at":"2024-11-14T00:27:21.167Z","updated_at":"2025-04-10T00:36:32.723Z","avatar_url":"https://github.com/vlopezferrando.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# PyMut\n\nPyMut is a Python 3 module that fills the gap between machine learning and\nbioinformatics, providing methods that help in the prediction of pathology in\nprotein mutations. Using PyMut you can compute features, train predictors,\nevaluate them, predict the pathology of mutations, select the best features...\n\n## Installation\n\nWe recommend using Anaconda, a free distribution of the SciPy stack. To install\nPyMut, follow these steps:\n\n1. \u003ca href=\"https://www.continuum.io/downloads\" target=\"_blank\"\u003e\nDownload anaconda with Python 3.5\u003c/a\u003e\nfor your platform (Windows, Linux or OSX), and install (double-click in Windows), or run in Linux of OSX:\n\n    `bash Anaconda3-4.3.0-Linux-x86_64.sh`\n\n2. Create an anaconda Python 3 environment (we will name it pymut), and activate the\nenvironment:\n\n    `conda create python=3 --name pymut`\n\n    `source activate pymut`\n\n3. Install PyMut:\n\n    `pip install pymut`\n\n## Tutorial\n\nVisit the\n\u003ca href=\"http://mmb.pcb.ub.es/pmut2017/PyMut-tutorial\" target=\"_blank\"\u003e\nPyMut tutorial\u003c/a\u003e\nto see a full example of usage of PyMut. In the tutorial we show how to:\n\n* Compute features that describe mutations and plot their distribution.\n* Train classifiers, evaluate them using cross-validation and plot their ROC curves.\n* Select the best features.\n* Train a pathology predictor.\n* Predict the pathology of mutations using our newly trained predictor.\n\n## Sample plots generated by PyMut\n\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/01.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/08.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/09.png\"\u003e\n\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/03.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/04.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/05.png\"\u003e\n\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/06.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/02.png\"\u003e\n\u003cimg width=\"200px\" src=\"http://mmb.pcb.ub.es/pmut2017/static/img/pymut/07.png\"\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvlopezferrando%2Fpymut","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvlopezferrando%2Fpymut","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvlopezferrando%2Fpymut/lists"}