{"id":35762837,"url":"https://github.com/bionetslab/robust_bias_aware","last_synced_at":"2026-01-07T00:00:12.842Z","repository":{"id":65673216,"uuid":"501535191","full_name":"bionetslab/robust_bias_aware","owner":"bionetslab","description":"Study-bias-aware, robust disease module mining","archived":false,"fork":false,"pushed_at":"2025-08-28T11:52:30.000Z","size":1548373,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-09-05T18:35:54.113Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://robust-web.net/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bionetslab.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}},"created_at":"2022-06-09T06:42:43.000Z","updated_at":"2025-08-28T11:52:36.000Z","dependencies_parsed_at":"2023-02-18T07:45:28.503Z","dependency_job_id":null,"html_url":"https://github.com/bionetslab/robust_bias_aware","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/bionetslab/robust_bias_aware","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bionetslab%2Frobust_bias_aware","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bionetslab%2Frobust_bias_aware/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bionetslab%2Frobust_bias_aware/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bionetslab%2Frobust_bias_aware/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bionetslab","download_url":"https://codeload.github.com/bionetslab/robust_bias_aware/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bionetslab%2Frobust_bias_aware/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28230228,"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":"2026-01-06T02:00:07.049Z","response_time":56,"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":[],"created_at":"2026-01-07T00:00:05.945Z","updated_at":"2026-01-07T00:00:12.834Z","avatar_url":"https://github.com/bionetslab.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Installation\n\nInstall conda environment as follows (there also exists an environment.yml but it contains more packages than necessary)\n```bash\nconda create --name robust python=3.7\nconda activate robust\nconda install numpy matplotlib pandas networkx pip jupyter\npip install pcst_fast\n```\nNote that Python 3.7 is a hard requirement!\n\n# Running ROBUST\n\nNavigate to home path '/robust_bias_aware', then you can simply run robust by calling \n```bash\npython3 robust.py ./data/data-case-study-1-covid-19/covid-19-seeds.txt covid19.graphml\n\npython3 robust.py ./data/data-case-study-2-prec-puberty/prec-pub-seeds.txt prec_puberty.graphml --namespace UNIPROT\n```\nThe positional arguments are:\n```\n\n[1] file with a list of seed genes (delimiter: newline-separated)\n[2] path to output file (supported output file types: .graphml, .csv, others) [read more below]\n\n\nThe suffix of the path to the output file you specify, determine the format of the output.\nYou can either choose\n- .graphml: A .graphml file is written that contains the following vertex properties: isSeed, significance, nrOfOccurrences, connected_components_id, trees\n- .csv: A .csv file which contains a vertex table with #occurrences, %occurrences, terminal (isSeed) \n- everything else: An edge list\n\n```\nThe optional arguments are:\n```\n\n[1] --network NETWORK\t\t\t\t\tDescription: Specify path to graph or identifier of networks shipped with ROBUST ('BioGRID', 'APID', 'STRING'), type=str or file (allowed types: .graphml, .txt, .csv, .tsv), default: 'BioGRID' [read more below]\n\nNetwork input options:\n\t- A two-column edgelist. File types and corresponding delimiters are as follows: 1. '.txt' file should be space-separated 2. '.tsv' file should be tab-separated 3. '.csv' file should be comma-separated. No other file  formats except '.txt', '.csv' and '.tsv' are accepted at the moment.\n\t- A valid .graphml file\n\t- In-built network name {'BioGRID', 'APID', 'STRING'}\n\n\n[2] --alpha ALPHA\t\t\t\t\tDescription: initial fraction for ROBUST, type=float, expected range=[0,1], default: 0.25\n\n[3] --beta BETA\t\t\t\t\t\tDescription: reduction factor for ROBUST, type=float, expected range=[0,1], default: 0.90\n\n[4] --n N\t\t\t\t\t\tDescription: # of steiner trees for ROBUST, type=int, expected range=(0,+inf], default: 30\n\n[5] --tau TAU\t\t\t\t\t\tDescription: threshold value for ROBUST, type=float, expected range=(0,1], default: 0.1\n\n[6] --namespace {'ENTREZ', 'GENE_SYMBOL', 'UNIPROT'}\tDescription: gene/ protein identifier options for study bias data, type=str, default: 'GENE_SYMBOL'\n\n[7] --study-bias-scores\t\t\t\t\tDescription: specify edge weight function used by ROBUST, type=str, default: 'BAIT_USAGE' [read more below]\n\nStudy bias score input options:\n\t- A two-column file (delimiter: comma), where the first column is the gene or protein name (column datatype: string) and the second column is the study bias score (column datatype: int).\n\t- In-built study-bias-score options {'NONE' or 'None', 'BAIT_USAGE', 'STUDY_ATTENTION'} ('NONE' or 'None' leads to running ROBUST with uniform edge costs.)\n\n\n--gamma\t\t\t\t\t\t\tDescription: Hyper-parameter gamma used by bias-aware edge weights. This hyperparameter regulates to what extent the study bias data is being leveraged when running ROBUST., type=float, expected range=[0,1], default: 1.00\n```\n\n# Updating in-built PPI networks\n```bash\npython3 ./data/networks/update_inbuilt_ppi_networks.py\n```\n\n# Updating study bias scores\n```bash\npython3 ./data/study_bias_scores/update_inbuilt_study_bias_scores.py\n```\n\n# Evaluating ROBUST\n\nFor a large-scale empirical evaluation of ROBUST, please follow the instructions given here: https://github.com/bionetslab/robust-eval.\n\n# Citing ROBUST-Web\n\nPlease cite ROBUST as follows:\n- S. Sarkar, M. Lucchetta, A. Maier, M. M. Abdrabbou, J. Baumbach, M. List, M. H. Schaefer, D. B. Blumenthal: *Online bias-aware disease module mining with ROBUST-Web*, **Bioinformatics 35(6)**, 26 May 2023, https://doi.org/10.1093/bioinformatics/btad345.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbionetslab%2Frobust_bias_aware","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbionetslab%2Frobust_bias_aware","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbionetslab%2Frobust_bias_aware/lists"}