{"id":"W4413022823","doi":"10.1007/s12672-025-03340-2","title":"Identification of copper related biomarkers in breast cancer using machine learning","year":2025,"lang":"en","type":"article","venue":"Discover Oncology","topic":"Ferroptosis and cancer prognosis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China; Harbin Medical University Cancer Hospital","keywords":"Breast cancer; Identification (biology); Copper; Computational biology; Artificial intelligence; Machine learning; Computer science; Cancer; Medicine; Internal medicine; Biology; Materials science; Metallurgy; Botany","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":[0.0002182773,0.00007887089,0.0002748907,0.000198918,0.00003011467,0.000005251279,0.00005056621,0.0001114518,0.0001842225],"category_scores_gemma":[0.00002909254,0.00006975113,0.00005956301,0.0004612474,0.00008559856,0.00006360934,0.00003461476,0.0001628479,0.000002901622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003763826,"about_ca_system_score_gemma":0.0003458598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002093546,"about_ca_topic_score_gemma":0.0002587943,"domain_scores_codex":[0.9991381,0.00008050779,0.0003670067,0.0001906068,0.00008580204,0.000138023],"domain_scores_gemma":[0.9996315,0.00003333413,0.0001356716,0.0001151508,0.00005897166,0.00002533272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003924733,0.0002081687,0.8391041,0.0000977294,0.0001921526,0.000006138073,0.0002411578,0.0003409468,0.1499748,0.000240961,0.0002635607,0.008937821],"study_design_scores_gemma":[0.003711944,0.0001491834,0.9518142,0.0004805323,0.0003575161,0.00002883687,0.0006268414,0.01962562,0.0196982,0.00008454417,0.003277991,0.0001445819],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928849,0.001235119,0.00004620546,0.003605201,0.000269305,0.0002067702,0.00002076675,0.00001367628,0.001718098],"genre_scores_gemma":[0.9988247,0.0004060743,0.00003940464,0.0002412216,0.0000137987,0.00002880642,0.0000197923,0.00000931363,0.0004168682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1302766,"threshold_uncertainty_score":0.3164829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661789709673629,"score_gpt":0.3413882913746684,"score_spread":0.3247703942779321,"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."}}