{"id":"W2513310700","doi":"10.1016/j.exphem.2016.06.020","title":"Targeted therapy in aml: looking beyond mutations with leucegene project","year":2016,"lang":"en","type":"article","venue":"Experimental Hematology","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Exploit; Computational biology; Session (web analytics); RNA; Drug discovery; Key (lock); Human genome; Biology; Genome; Computer science; Bioinformatics; Gene; Genetics; World Wide Web","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":[0.0008594962,0.0002957146,0.0005101458,0.0003175612,0.0002442265,0.001244566,0.000552327,0.0005873383,0.003396656],"category_scores_gemma":[0.0005708187,0.0001001559,0.0003586424,0.0002608223,0.0004056656,0.0009238683,0.0006533138,0.001093541,0.0005209781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007867159,"about_ca_system_score_gemma":0.0006069145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004016378,"about_ca_topic_score_gemma":0.0009431541,"domain_scores_codex":[0.9997057,0.00008685335,0.0000159594,0.00004016842,0.00007335077,0.0000779283],"domain_scores_gemma":[0.9998392,0.00004235389,0.00003111553,0.00002094841,0.00002299618,0.00004341183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00533385,0.002419022,0.01367021,0.0007685133,0.0006608368,0.0009417768,0.000255922,0.005830711,0.2140206,0.03736421,0.01451261,0.7042218],"study_design_scores_gemma":[0.003506519,0.02625395,0.02862799,0.0007435087,0.001581243,0.006056145,0.0006262652,0.009574212,0.3586724,0.04165141,0.5225806,0.00012582],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8202347,0.09879935,0.01199317,0.02357253,0.0008547276,0.000465771,0.001459473,0.000535954,0.04208443],"genre_scores_gemma":[0.9403657,0.04046073,0.00566467,0.004054464,0.0002046952,0.0001776953,0.0008982187,0.00008467152,0.008089193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003396656,"threshold_uncertainty_score":0.01136297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02370705408889644,"score_gpt":0.3400748200189138,"score_spread":0.3163677659300174,"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."}}