{"id":"W4385983130","doi":"10.1016/j.exphem.2023.06.130","title":"3023 – ATLAS-AML: AN AUTOMATED BIOINFORMATICS PIPELINE FOR TARGET CHARACTERIZATION IN ACUTE MYELOID LEUKEMIA","year":2023,"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":"Princess Margaret Cancer Centre; University Health Network","funders":"","keywords":"Myeloid leukemia; Transcriptome; Computational biology; Bioinformatics; Leukemia; Myeloid; Atlas (anatomy); Biology; Cancer research; Medicine; Gene; Immunology; Gene expression; Genetics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003220978,0.0002883885,0.0006460625,0.0005891575,0.0001169999,0.0000288034,0.000230955,0.0003177681,0.0002400162],"category_scores_gemma":[0.00008844545,0.0002734543,0.0001001326,0.0007932505,0.000141084,0.0003448139,0.0001648827,0.0002624048,0.0005696293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007485901,"about_ca_system_score_gemma":0.0003706465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002914071,"about_ca_topic_score_gemma":0.000005481277,"domain_scores_codex":[0.9977543,0.00006928633,0.0007090209,0.0004058859,0.0002891597,0.0007723544],"domain_scores_gemma":[0.9989914,0.00009882618,0.0001435605,0.0004229002,0.00009597839,0.0002473349],"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":[0.002560526,0.0007372883,0.006822953,0.0006017233,0.0002275163,0.0008986355,0.005636172,0.00005382253,0.9360141,0.007217844,0.0368384,0.002390988],"study_design_scores_gemma":[0.004105171,0.0004817228,0.002468296,0.0000591481,0.00001798257,0.0004861036,0.001137411,0.4843404,0.5038354,0.00004449854,0.002783825,0.0002400276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929149,0.0001600468,0.0014767,0.001016915,0.0004352107,0.001823992,0.0001846239,0.001410202,0.0005774602],"genre_scores_gemma":[0.972953,0.00008955283,0.01173519,0.0008406693,0.0001771462,0.0007169563,0.01144107,0.0001280345,0.001918442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4842866,"threshold_uncertainty_score":0.9999717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02483181063886628,"score_gpt":0.3559312414755823,"score_spread":0.331099430836716,"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."}}