{"id":21151098,"url":"https://github.com/emma-wilson/in-vitro-screening","last_synced_at":"2025-10-05T12:12:56.447Z","repository":{"id":115125570,"uuid":"513883885","full_name":"emma-wilson/in-vitro-screening","owner":"emma-wilson","description":"Open data and code for published paper.","archived":false,"fork":false,"pushed_at":"2023-02-07T18:06:55.000Z","size":4530,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-14T14:26:07.962Z","etag":null,"topics":["machine-learning","open-code","open-data","screening"],"latest_commit_sha":null,"homepage":"https://doi.org/10.1042/CS20220594","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/emma-wilson.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-07-14T11:59:52.000Z","updated_at":"2023-02-07T18:05:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"d0717611-6b80-44f1-b0ef-e106b38e079f","html_url":"https://github.com/emma-wilson/in-vitro-screening","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/emma-wilson/in-vitro-screening","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/emma-wilson%2Fin-vitro-screening","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/emma-wilson%2Fin-vitro-screening/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/emma-wilson%2Fin-vitro-screening/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/emma-wilson%2Fin-vitro-screening/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/emma-wilson","download_url":"https://codeload.github.com/emma-wilson/in-vitro-screening/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/emma-wilson%2Fin-vitro-screening/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":278452514,"owners_count":25989188,"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":"2025-10-05T02:00:06.059Z","response_time":54,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","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":["machine-learning","open-code","open-data","screening"],"created_at":"2024-11-20T10:13:42.361Z","updated_at":"2025-10-05T12:12:56.409Z","avatar_url":"https://github.com/emma-wilson.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# In Vitro Screening Comparison\n\nData and code to accompany the paper [\"Screening for in vitro systematic reviews: a comparison of screening methods and training of a machine learning classifier\"](https://doi.org/10.1042/CS20220594) published in *Clinical Science*.\n\n## Code scripts\n\nAll code is written in R using R Markdown documents. R version number and package version numbers are included in each script. Details of each script are below. Run each script in order.\n\n- **1-1_data cleaning.Rmd:** Prepare screening method comparison data for analysis; remove excluded data\n- **1-2_calculate_performance.Rmd:** Calaculate the performace (sensitivity and specificity) of screening methods at various thresholds\n- **1-3_analyse_performance.Rmd:** Plot the performance in a ROC curve and determine the optimal threshold for regex screening methods\n- **1-4_search_term_retrieval_comparison.Rmd:** Additional analysis comparing retrieval of studies using the planned vs actual search terms\n\n## Data\n\nData are stored in the following folders:\n\n- **data-raw:** raw data to be processed\n- **data:** data which have undergone some processing\n- **data-analysis:** final clean datasets\n- **data-ml_input:** input data required to train ML\n- **data-ml_output:** output data from ML\n\n## Functions\n\nMachine learning (ML) functions are in the `functions` folder. Please not that the information required to configure the ML API are **not** included as we do not have permission to share this.\n\n## Plots\n\nPlot outputs (in PDF file format) are in the `figures` folder.\n\n- **regex_histogram.pdf:** histograms showing number of regex matches against (a) tiab and (b) full text\n- **screening_roc.pdf:** figure from screening comparison part of project\n- **ml_roc.pdf:** figure from machine learning part of project\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femma-wilson%2Fin-vitro-screening","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Femma-wilson%2Fin-vitro-screening","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Femma-wilson%2Fin-vitro-screening/lists"}