{"id":"W4310159356","doi":"10.1182/blood-2022-159069","title":"Deep Multi-Omics Profiling in Cytogenetically Poor-Risk AML","year":2022,"lang":"en","type":"article","venue":"Blood","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Princess Margaret Cancer Centre; University Health Network; Institute for Research in Immunology and Cancer; Centre Hospitalier Universitaire Sainte-Justine","funders":"Incyte; Celgene; Pfizer; Astellas Pharma; Bristol-Myers Squibb","keywords":"Gene expression profiling; Profiling (computer programming); Computational biology; Biology; Bioinformatics; Medicine; Genetics; Computer science; Gene; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001005987,0.0005091465,0.0007343247,0.001873544,0.0005659906,0.002688166,0.0005455537,0.0008650013,0.007509992],"category_scores_gemma":[0.003580007,0.0002201069,0.0005160435,0.001999852,0.0003064139,0.0009542393,0.001639786,0.0007351303,0.001835233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005000889,"about_ca_system_score_gemma":0.0008384835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261407,"about_ca_topic_score_gemma":0.003038972,"domain_scores_codex":[0.9991564,0.0001287344,0.00009964233,0.0002382634,0.0001844336,0.0001926155],"domain_scores_gemma":[0.998626,0.0005207087,0.000332682,0.000192401,0.0001840275,0.0001441796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002940516,0.0001849103,0.5444337,0.001676832,0.001291345,0.001394934,0.0007723942,0.003576641,0.1115949,0.005135658,0.03815272,0.2888455],"study_design_scores_gemma":[0.0002207078,0.0005541714,0.687147,0.001207298,0.003335595,0.004874121,0.002578733,0.01293263,0.0436042,0.02637488,0.2170148,0.0001558871],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9013298,0.01599605,0.004259277,0.006564139,0.000462993,0.00005693914,0.0574555,0.0003951962,0.01348007],"genre_scores_gemma":[0.9559454,0.004129774,0.003538536,0.001730547,0.0003473069,0.0000696596,0.02957415,0.0001278545,0.004536725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007509992,"threshold_uncertainty_score":0.02512336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02306219860393496,"score_gpt":0.2923445585423202,"score_spread":0.2692823599383853,"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."}}