{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004293398,0.0001952118,0.0001914067,0.0001662834,0.00006338857,0.0001331484,0.0002830135,0.000228076,0.00001857378],"category_scores_gemma":[0.0001163653,0.0001690147,0.00008878225,0.0002236048,0.00008553058,0.00002466712,0.0001139388,0.0001348375,0.00004471281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003044238,"about_ca_system_score_gemma":0.0003210873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009239114,"about_ca_topic_score_gemma":0.00002464334,"domain_scores_codex":[0.9985191,0.00003052253,0.0005030414,0.0002641628,0.0002485849,0.0004345498],"domain_scores_gemma":[0.9993276,0.00001980889,0.00006430456,0.0002951237,0.0001144399,0.0001787261],"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":[0.0001195418,0.0003289407,0.0006035049,0.0007513056,0.000178403,0.00001658969,0.0007891039,0.00003809643,0.9649888,0.00006286402,0.008455561,0.02366729],"study_design_scores_gemma":[0.002156704,0.0005523285,0.001672159,0.0001061765,0.00007968149,0.00003296996,0.000184467,0.4449205,0.4548522,0.0001045432,0.09487067,0.0004676626],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796935,0.0003412543,0.01698331,0.0003921121,0.0004309122,0.0007444072,0.0007681086,0.0002043742,0.000442027],"genre_scores_gemma":[0.9474965,0.001838226,0.02589937,0.0006261682,0.000939489,0.0001584167,0.01975876,0.00009077315,0.003192304],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5101366,"threshold_uncertainty_score":0.6892221,"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."}}