{"id":"W4412163836","doi":"10.1158/1557-3265.aimachine-b044","title":"Abstract B044: Distributed Transcriptomic Modeling for Biomarker Discovery in Immuno-Oncology","year":2025,"lang":"en","type":"article","venue":"Clinical Cancer Research","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Biomarker discovery; Transcriptome; Precision oncology; Medicine; Biomarker; Computational biology; Oncology; Cancer; Biology; Internal medicine; Proteomics; Gene; Genetics; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002106335,0.000891384,0.0008864428,0.0005888035,0.0004438806,0.001205255,0.001534553,0.0008635588,0.002186013],"category_scores_gemma":[0.003392831,0.0005494994,0.001621065,0.0008990746,0.0006229565,0.000678386,0.0009928363,0.001308068,0.0005863425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001177121,"about_ca_system_score_gemma":0.001802352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01718783,"about_ca_topic_score_gemma":0.01241266,"domain_scores_codex":[0.9993948,0.0002159277,0.00002389228,0.0002420345,0.0000787001,0.00004458126],"domain_scores_gemma":[0.9988422,0.0006706858,0.00009960263,0.0001489321,0.0001731294,0.00006557866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002823156,0.00009331165,0.006406969,0.00008762258,0.0001965566,0.00009350148,0.00004754702,0.9649137,0.002922949,0.00229125,0.002187772,0.02047664],"study_design_scores_gemma":[0.00002082505,0.00001697098,0.0003953268,0.000002663954,0.00001128737,0.000006614776,0.000006371957,0.9962668,0.0003936459,0.002461549,0.0004150184,0.000002911996],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.1129494,0.0007206762,0.8721153,0.00140493,0.0001528999,0.0001774837,0.00398645,0.007047641,0.001445186],"genre_scores_gemma":[0.7553355,0.0004180531,0.2355084,0.0004053872,0.000124282,0.0003954765,0.005231536,0.0003972702,0.002184231],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01718783,"threshold_uncertainty_score":0.03417557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2421779387256747,"score_gpt":0.550050595073161,"score_spread":0.3078726563474863,"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."}}