{"id":"W3158922343","doi":"10.1038/s41467-021-22625-y","title":"A clinical transcriptome approach to patient stratification and therapy selection in acute myeloid leukemia","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"BC Cancer Agency; National Cancer Institute; BC Cancer Foundation; Terry Fox Research Institute; Genome British Columbia; National Human Genome Research Institute; Leukemia and Lymphoma Society of Canada; Knight Cancer Institute, Oregon Health and Science University; Oregon Health and Science University; Provincial Health Services Authority; Leukemia and Lymphoma Society","keywords":"Myeloid leukemia; Transcriptome; Risk stratification; Selection (genetic algorithm); Medicine; Computational biology; Leukemia; Myeloid; Stratification (seeds); Bioinformatics; Intensive care medicine; Oncology; Biology; Computer science; Cancer research; Internal medicine; Genetics; Gene; Artificial intelligence; Gene expression","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":[0.001790052,0.000370326,0.0005022857,0.001010084,0.0002838154,0.001124229,0.0003086286,0.0003299992,0.0008820363],"category_scores_gemma":[0.00453158,0.0001378693,0.0002614823,0.00076358,0.0003555957,0.0004841942,0.000712502,0.000894781,0.0004862767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003613233,"about_ca_system_score_gemma":0.0004812495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004405114,"about_ca_topic_score_gemma":0.0007816483,"domain_scores_codex":[0.9990908,0.000479041,0.00007229638,0.0001394022,0.0001678149,0.00005066912],"domain_scores_gemma":[0.9985982,0.0005997831,0.0003335203,0.000162856,0.0001939325,0.000111782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001399066,0.0002895715,0.4491716,0.000408142,0.0003772728,0.0009088833,0.0007544309,0.008734578,0.1264443,0.007013695,0.01157761,0.3929209],"study_design_scores_gemma":[0.0001970739,0.001709344,0.7301994,0.0004143754,0.0005119873,0.004985903,0.001633869,0.08034924,0.07881521,0.04604687,0.05497352,0.0001632668],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7406278,0.007717669,0.2156278,0.0124103,0.0004820636,0.0004274315,0.007201302,0.001306636,0.01419894],"genre_scores_gemma":[0.9410972,0.001944335,0.05136422,0.001758075,0.000263432,0.0002175756,0.002198582,0.00007982608,0.001076779],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001790052,"threshold_uncertainty_score":0.009466767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04978647121744192,"score_gpt":0.3745982779622205,"score_spread":0.3248118067447786,"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."}}