{"id":23442577,"url":"https://github.com/mvdoc/gfusion","last_synced_at":"2025-07-22T12:33:49.757Z","repository":{"id":75577294,"uuid":"67640156","full_name":"mvdoc/gfusion","owner":"mvdoc","description":"Implementation of DDR method to densify graphs using similarities between two sources","archived":false,"fork":false,"pushed_at":"2016-09-09T23:15:27.000Z","size":33,"stargazers_count":2,"open_issues_count":1,"forks_count":2,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-09T21:49:37.678Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/mvdoc.png","metadata":{"files":{"readme":"README.md","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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-09-07T20:15:15.000Z","updated_at":"2019-09-18T12:30:21.000Z","dependencies_parsed_at":"2023-06-06T23:30:38.711Z","dependency_job_id":null,"html_url":"https://github.com/mvdoc/gfusion","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mvdoc/gfusion","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mvdoc%2Fgfusion","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mvdoc%2Fgfusion/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mvdoc%2Fgfusion/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mvdoc%2Fgfusion/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mvdoc","download_url":"https://codeload.github.com/mvdoc/gfusion/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mvdoc%2Fgfusion/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266496399,"owners_count":23938711,"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","status":"online","status_checked_at":"2025-07-22T02:00:09.085Z","response_time":66,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":[],"created_at":"2024-12-23T17:32:57.998Z","updated_at":"2025-07-22T12:33:49.692Z","avatar_url":"https://github.com/mvdoc.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# gfusion\n[![Build\nStatus](https://travis-ci.org/mvdoc/gfusion.svg?branch=master)](https://travis-ci.org/mvdoc/gfusion)\n[![Coverage\nStatus](https://coveralls.io/repos/github/mvdoc/gfusion/badge.svg?branch=master)](https://coveralls.io/github/mvdoc/gfusion?branch=master)\n[![codecov](https://codecov.io/gh/mvdoc/gfusion/branch/master/graph/badge.svg)](https://codecov.io/gh/mvdoc/gfusion)\n\nImplementation of DDR method to densify graphs using similarities\nbetween two sources\n\nSee Zhang, Ping, Fei Wang, and Jianying Hu. \"Towards drug repositioning:\na unified computational framework for integrating multiple aspects of\ndrug similarity and disease similarity.\" AMIA Annu Symp Proc. 2014.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmvdoc%2Fgfusion","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmvdoc%2Fgfusion","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmvdoc%2Fgfusion/lists"}