{"id":"W4327569177","doi":"10.1038/s41587-023-01696-w","title":"Contamination source modeling with SCRuB improves cancer phenotype prediction from microbiome data","year":2023,"lang":"en","type":"article","venue":"Nature Biotechnology","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":80,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute; Canadian Institute for Advanced Research; U.S. Department of Health and Human Services","keywords":"Microbiome; Contamination; In silico; Robustness (evolution); Computer science; Computational biology; Biology; Bioinformatics; Ecology; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001747687,0.001401908,0.001133955,0.001044809,0.0005481322,0.001372675,0.0008608539,0.001035675,0.001517454],"category_scores_gemma":[0.005927584,0.0004523706,0.002000927,0.0008318063,0.0003130148,0.0009772046,0.001324516,0.001158418,0.0009235985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003860025,"about_ca_system_score_gemma":0.001165661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0072571,"about_ca_topic_score_gemma":0.008590722,"domain_scores_codex":[0.9994231,0.0001954938,0.00002734789,0.0001983621,0.0001018355,0.000053793],"domain_scores_gemma":[0.9970643,0.001838356,0.0002153478,0.0004566423,0.0003145801,0.0001106716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002319348,0.0005790638,0.09454746,0.0004691041,0.001096622,0.0009310188,0.0001872079,0.649087,0.02243199,0.001460677,0.01295829,0.2139323],"study_design_scores_gemma":[0.00002613907,0.00008437233,0.002245253,0.00001565227,0.00006771682,0.00008235798,0.00002733639,0.9900232,0.004341196,0.001855017,0.001218243,0.00001345746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3615402,0.001664598,0.6119858,0.0008677825,0.000275332,0.0001073242,0.00643967,0.01554751,0.001571853],"genre_scores_gemma":[0.8319248,0.0004190267,0.1525486,0.0004281412,0.0001009763,0.0001025198,0.01195212,0.0008862149,0.001637688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0072571,"threshold_uncertainty_score":0.01442975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01241199143986364,"score_gpt":0.2707195113540372,"score_spread":0.2583075199141735,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}