{"id":15041250,"url":"https://github.com/pathwayforte/pathway-forte","last_synced_at":"2025-06-17T22:33:46.171Z","repository":{"id":61598310,"uuid":"178654585","full_name":"pathwayforte/pathway-forte","owner":"pathwayforte","description":"A Python package for benchmarking pathway database with functional enrichment and classification methods","archived":false,"fork":false,"pushed_at":"2021-03-02T09:31:36.000Z","size":2234,"stargazers_count":13,"open_issues_count":2,"forks_count":6,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-05-02T02:19:42.052Z","etag":null,"topics":["benchmarking","bioinformatics","databases","machine-learning","pathway-analysis","pathway-enrichment-analysis","systems-biology"],"latest_commit_sha":null,"homepage":"https://pathwayforte.readthedocs.io/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/pathwayforte.png","metadata":{"files":{"readme":"README.rst","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-03-31T07:08:46.000Z","updated_at":"2024-02-26T10:49:27.000Z","dependencies_parsed_at":"2022-10-19T23:00:19.413Z","dependency_job_id":null,"html_url":"https://github.com/pathwayforte/pathway-forte","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"purl":"pkg:github/pathwayforte/pathway-forte","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pathwayforte%2Fpathway-forte","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pathwayforte%2Fpathway-forte/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pathwayforte%2Fpathway-forte/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pathwayforte%2Fpathway-forte/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pathwayforte","download_url":"https://codeload.github.com/pathwayforte/pathway-forte/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pathwayforte%2Fpathway-forte/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":260450222,"owners_count":23011029,"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":["benchmarking","bioinformatics","databases","machine-learning","pathway-analysis","pathway-enrichment-analysis","systems-biology"],"created_at":"2024-09-24T20:45:48.832Z","updated_at":"2025-06-17T22:33:41.136Z","avatar_url":"https://github.com/pathwayforte.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"PathwayForte |build| |docs| |coverage| |zenodo|\n===============================================\nA Python package for benchmarking pathway databases with functional enrichment and prediction methods\ntasks.\n\nIf you find ``pathway_forte`` useful for your work, please consider citing:\n\n.. [1] Mubeen, S., *et al* (2019). `The Impact of Pathway Database Choice on\n       Statistical Enrichment Analysis and Predictive Modeling\n       \u003chttps://doi.org/10.3389/fgene.2019.01203\u003e`_. *Front. Genet.*, 10:1203.\n\n\nInstallation |pypi_version| |python_versions| |pypi_license|\n------------------------------------------------------------\n``pathway_forte`` can be installed from `PyPI \u003chttps://pypi.org/project/pathway-forte\u003e`_\nwith the following command in your terminal:\n\n.. code-block:: sh\n\n    $ python3 -m pip install pathway_forte\n\nThe latest code can be installed from `GitHub \u003chttps://github.com/pathwayforte/pathway-forte\u003e`_\nwith:\n\n.. code-block:: sh\n\n    $ python3 -m pip install git+https://github.com/pathwayforte/pathway-forte.git\n\nFor developers, the code can be installed with:\n\n.. code-block:: sh\n\n    $ git clone https://github.com/pathwayforte/pathway-forte.git\n    $ cd pathway-forte\n    $ python3 -m pip install -e .\n\nMain Commands\n-------------\nThe table below lists the main commands of PathwayForte.\n\n+------------+--------------------------------+\n| Command    | Action                         |\n+============+================================+\n| datasets   | Lists of Cancer Datasets       |\n+------------+--------------------------------+\n| export     | Export Gene Sets using ComPath |\n+------------+--------------------------------+\n| ora        | List of ORA Analyses           |\n+------------+--------------------------------+\n| fcs        | List of FCS Analyses           |\n+------------+--------------------------------+\n| prediction | List of Prediction Methods     |\n+------------+--------------------------------+\n\nFunctional Enrichment Methods\n-----------------------------\n- **ora**. Lists Over-Representation Analyses (e.g., one-tailed hyper-geometric test).\n- **fcs**. Lists Functional Class Score Analyses such as GSEA and ssGSEA using\n  `GSEAPy \u003chttps://github.com/ostrokach/gseapy\u003e`_.\n\nPrediction Methods\n------------------\n``pathway_forte`` enables three classification methods (i.e., binary classification, training SVMs for\nmulti-classification tasks, or survival analysis) using individualized pathway activity scores. The scores can be\ncalculated from any pathway with a variety of tools (see [2]_) using any pathway database that enables to export its\ngene sets.\n\n- **binary**. Trains an elastic net model for a binary classification task (e.g., tumor vs. normal patients). The\n  training is conducted using a nested cross validation approach (the number of cross validation in both loops can be\n  selected). The model used can be easily changed since most of the models in\n  `scikit-learn \u003chttps://scikit-learn.org/\u003e`_ (the machine learning library used by this package) required the same\n  input.\n- **subtype**. Trains a SVM model for a multi-class classification task (e.g., predict tumor subtypes). The training is\n  conducted using a nested cross validation approach (the number of cross validation in both loops can be selected).\n  Similarly as the previous classification task, other models can quickly be implemented.\n- **survival**. Trains a Cox's proportional hazard's model with elastic net penalty. The training is conducted using a\n  nested cross validation approach with a grid search in the inner loop. This analysis requires pathway activity\n  scores, patient classes and lifetime patient information.\n\nOther\n-----\n- **export**. Export GMT files with current gene sets for the pathway databases included in ComPath [3]_.\n- **datasets**. Lists the TCGA data sets [4]_ that are ready to run in ``pathway_forte``.\n\nReferences\n----------\n.. [2] Lim, S., *et al.* (2018). `Comprehensive and critical evaluation of individualized pathway activity measurement\n       tools on pan-cancer data \u003chttps://doi.org/10.1093/bib/bby097\u003e`_. *Briefings in bioinformatics*, bby125.\n.. [3] Domingo-Fernández, D., *et al.* (2018). `ComPath: An ecosystem for exploring, analyzing, and curating mappings\n       across pathway databases \u003chttps://doi.org/10.1038/s41540-018-0078-8\u003e`_. *npj Syst Biol Appl.*, 4(1):43.\n.. [4] Weinstein, J. N., *et al.* (2013). `The cancer genome atlas pan-cancer analysis project\n       \u003chttps://doi.org/10.1038/ng.2764\u003e`_. *Nature genetics*, 45(10), 1113.\n\nLicense\n-------\nThe Pathway Forte logo is derived from `\"Muscle Fat\" \u003chttps://game-icons.net/1x1/lorc/muscle-fat.html\u003e`_ by Lorc, used under CC BY 3.0.\n\nDisclaimer\n-----------\nPathForte is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or\nguarantee of maintenance, support, or back-up of data.\n\n.. |build| image:: https://travis-ci.com/pathwayforte/pathway-forte.svg?branch=master\n    :target: https://travis-ci.com/pathwayforte/pathway-forte\n    :alt: Build Status\n\n.. |docs| image:: http://readthedocs.org/projects/pathwayforte/badge/?version=latest\n    :target: https://pathwayforte.readthedocs.io/en/latest/\n    :alt: Documentation Status\n\n.. |coverage| image:: https://codecov.io/gh/pathwayforte/pathway-forte/coverage.svg?branch=master\n    :target: https://codecov.io/gh/pathwayforte/pathway-forte?branch=master\n    :alt: Coverage Status\n\n.. |python_versions| image:: https://img.shields.io/pypi/pyversions/pathway_forte.svg\n    :target: https://pypi.org/project/pathway-forte\n    :alt: Stable Supported Python Versions\n\n.. |pypi_version| image:: https://img.shields.io/pypi/v/pathway_forte.svg\n    :target: https://pypi.org/project/pathway-forte\n    :alt: Current version on PyPI\n\n.. |pypi_license| image:: https://img.shields.io/pypi/l/pathway_forte.svg\n    :target: https://github.com/pathwayforte/pathway-forte/blob/master/LICENSE\n    :alt: Apache-2.0\n\n.. |zenodo| image:: https://zenodo.org/badge/178654585.svg\n    :target: https://zenodo.org/badge/latestdoi/178654585\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpathwayforte%2Fpathway-forte","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpathwayforte%2Fpathway-forte","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpathwayforte%2Fpathway-forte/lists"}