{"id":"W4327747318","doi":"10.1038/s41591-023-02221-x","title":"Diagnostic classification of childhood cancer using multiscale transcriptomics","year":2023,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sinai Health System; Princess Margaret Cancer Centre; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; SickKids Foundation; University of Toronto; Hospital for Sick Children","funders":"Garron Family Cancer Centre; Ontario Ministry of Research and Innovation; St. Baldrick's Foundation; Canadian Institutes of Health Research; Hospital for Sick Children; V Foundation for Cancer Research","keywords":"Childhood cancer; Optimal distinctiveness theory; Computational biology; Cancer; Transcriptome; Classifier (UML); Bioinformatics; Oncology; Medicine; Biology; Artificial intelligence; Computer science; Internal medicine; Genetics; Psychology; Gene expression; 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":[0.0006991546,0.0003399557,0.0003375003,0.001573637,0.0002636053,0.0007199218,0.0003020231,0.0003363433,0.0006071329],"category_scores_gemma":[0.001488714,0.0001196184,0.0006901379,0.0007506723,0.000231953,0.0003043607,0.0006410968,0.0004486587,0.0001997036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006873236,"about_ca_system_score_gemma":0.0006017129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005947279,"about_ca_topic_score_gemma":0.009566558,"domain_scores_codex":[0.9996449,0.00007299634,0.00002552257,0.0001295436,0.0000682164,0.00005887928],"domain_scores_gemma":[0.9995436,0.000178192,0.0001174284,0.00003943948,0.00008970228,0.00003155418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005319781,0.000120655,0.4968842,0.0002668625,0.0004234618,0.0005566131,0.0004605191,0.08086016,0.1622895,0.005729622,0.003857133,0.2480193],"study_design_scores_gemma":[0.00002483013,0.0001248602,0.2845115,0.00008508593,0.0002041491,0.0004848501,0.0003559446,0.6683047,0.02933261,0.01084146,0.005663738,0.00006617499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7738272,0.001062144,0.2176096,0.0004698051,0.00003968014,0.0001026772,0.004052667,0.0006662071,0.002170037],"genre_scores_gemma":[0.9236997,0.0002569848,0.07224315,0.00008680101,0.00002845964,0.0000701121,0.003120196,0.00005035303,0.0004442718],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005947279,"threshold_uncertainty_score":0.01182532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01520289199090626,"score_gpt":0.3064505224690396,"score_spread":0.2912476304781334,"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."}}