{"id":"W4405042153","doi":"10.1182/blood-2024-211344","title":"ATLAS-AML: An Automated Bioinformatics Pipeline for Drug Target Characterization in Acute Myeloid Leukemia","year":2024,"lang":"en","type":"article","venue":"Blood","topic":"Genetics, Bioinformatics, and Biomedical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Myeloid leukemia; Medicine; Atlas (anatomy); Leukemia; Drug; Bioinformatics; Myeloid; Computational biology; Cancer research; Biology; Internal medicine; Pharmacology","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":[0.001744594,0.001722233,0.00132518,0.00246457,0.0006983418,0.001692856,0.001304796,0.000721207,0.006512227],"category_scores_gemma":[0.004157513,0.0006683583,0.001907117,0.001832495,0.0004476178,0.001123981,0.001824684,0.001302642,0.00343091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001193104,"about_ca_system_score_gemma":0.003186515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005159508,"about_ca_topic_score_gemma":0.009156964,"domain_scores_codex":[0.9990012,0.0002116963,0.00009588416,0.0003826966,0.0002375302,0.00007100915],"domain_scores_gemma":[0.9987774,0.0006997994,0.0001412139,0.0001299911,0.0001610034,0.00009057238],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003660246,0.0003915572,0.0247181,0.003529792,0.00191641,0.00114623,0.0006431188,0.09622689,0.09254364,0.01356664,0.3242736,0.4373837],"study_design_scores_gemma":[0.0009770161,0.0005200143,0.01678825,0.0001659046,0.0004855709,0.0007332119,0.0002341701,0.6495109,0.06794531,0.05177712,0.2106162,0.0002464074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04476122,0.004083312,0.4407448,0.002209183,0.0002747228,0.0006755057,0.1762662,0.3251861,0.005798881],"genre_scores_gemma":[0.1930305,0.002086116,0.5379785,0.001805675,0.0001872852,0.001640141,0.2506642,0.008940942,0.003666779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006512227,"threshold_uncertainty_score":0.02178556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009587297429028542,"score_gpt":0.277429740822998,"score_spread":0.2678424433939695,"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."}}