{"id":"W4385231005","doi":"10.1002/pmic.202200401","title":"Towards deciphering glioblastoma intra‐tumoral heterogeneity: The importance of integrating multidimensional models","year":2023,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto","funders":"","keywords":"Glioblastoma; Phenotype; Biology; Computational biology; Genetic heterogeneity; Precision medicine; Glioma; Bioinformatics; Cancer research; 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":[0.0002485897,0.0001486892,0.0002597624,0.0000522087,0.00008178867,0.00001245386,0.00008621143,0.00005237569,0.00001374666],"category_scores_gemma":[0.0001152561,0.00009373928,0.0001263607,0.0003320853,0.00006386498,0.00006246172,0.0001128229,0.0001372828,0.00001493644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007842054,"about_ca_system_score_gemma":0.00009037132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005553179,"about_ca_topic_score_gemma":0.00008500863,"domain_scores_codex":[0.9989461,0.00002413601,0.0003105208,0.0002164881,0.0002622867,0.0002404571],"domain_scores_gemma":[0.9993491,0.00005621554,0.0001193475,0.00028485,0.0001125398,0.00007798261],"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":[0.001302485,0.001217955,0.440319,0.0004448791,0.0009634481,0.0009115926,0.003360276,0.01591311,0.4623989,0.01799209,0.001046269,0.05412999],"study_design_scores_gemma":[0.00214458,0.0005098429,0.03502404,0.0003262371,0.0001061022,0.0001543439,0.0004301969,0.2030009,0.7550876,0.002892267,0.0001091716,0.0002147325],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971185,0.0001657232,0.0006828725,0.001035807,0.00008478353,0.0006929074,0.00001506959,0.00007915378,0.0001251451],"genre_scores_gemma":[0.9813476,0.00004586786,0.01815325,0.0001476693,0.00006426187,0.0001934375,0.00001217648,0.00002572814,0.00001001825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.405295,"threshold_uncertainty_score":0.3822577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03442916814101461,"score_gpt":0.2928785777427124,"score_spread":0.2584494096016978,"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."}}