{"id":"W3129637558","doi":"10.1158/1078-0432.ccr-20-4834","title":"A Uniform Computational Approach Improved on Existing Pipelines to Reveal Microbiome Biomarkers of Nonresponse to Immune Checkpoint Inhibitors","year":2021,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Gut microbiota and health","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"National Center for Advancing Translational Sciences; National Cancer Institute; National Institutes of Health; Johns Hopkins University; International Association for the Study of Lung Cancer; Bristol-Myers Squibb","keywords":"Microbiome; Metagenomics; Biomarker; Computational biology; Immune system; Gut microbiome; Biology; Medicine; Cancer; Bioinformatics; Oncology; Immunology; Internal medicine; Genetics; Gene","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00352324,0.0001530785,0.0003259307,0.0001645867,0.0001387393,0.00003042951,0.0002951659,0.0002144845,0.00003259076],"category_scores_gemma":[0.001754581,0.0001445105,0.0001905822,0.0006564966,0.0001993264,0.000002880329,0.0004763152,0.000360701,0.00002587048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009225203,"about_ca_system_score_gemma":0.001267771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002745743,"about_ca_topic_score_gemma":0.00009316683,"domain_scores_codex":[0.9971861,0.0004979161,0.0008596707,0.0006818994,0.0002648895,0.0005095663],"domain_scores_gemma":[0.9980069,0.000253164,0.0001085055,0.0004531581,0.0008429417,0.0003353774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002417936,0.0003281955,0.0008303114,0.0001236886,0.00006397731,0.000005220264,0.0000854422,0.0001478256,0.9844228,0.00001473483,0.005772318,0.005787562],"study_design_scores_gemma":[0.003009077,0.003075826,0.05352617,0.0004077401,0.000019758,0.00002450617,0.0006602436,0.0003221005,0.8884968,0.00006754544,0.0498444,0.0005458581],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947813,0.000433498,0.0005468905,0.002843766,0.000241941,0.0005342294,0.0001481589,0.000007995296,0.0004622797],"genre_scores_gemma":[0.9833326,0.0001019874,0.01311127,0.000887821,0.0005498969,0.00007511322,0.0002447212,0.00003721346,0.001659323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09592602,"threshold_uncertainty_score":0.5892966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1575591069558231,"score_gpt":0.4892872831924687,"score_spread":0.3317281762366456,"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."}}