{"id":"W2969991859","doi":"10.1016/j.exphem.2019.06.405","title":"INTEGRATIVE EPIGENOMIC ANALYSIS TO IDENTIFY POTENTIAL BIOMARKERS IN KMT2A TRANSLOCATED ACUTE MYELOID LEUKEMIA","year":2019,"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; Institute for Research in Immunology and Cancer","funders":"","keywords":"Epigenomics; Myeloid leukemia; Biology; Computational biology; Myeloid; Leukemia; Cancer research; DNA methylation; Genetics; Gene","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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002876204,0.0003904874,0.001175076,0.001492541,0.00006501838,0.00002978722,0.0003754578,0.000308501,0.00264071],"category_scores_gemma":[0.00002554655,0.0003484415,0.0003787195,0.001945527,0.0001897452,0.0001454031,0.0001826501,0.0004854001,0.001719185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065899,"about_ca_system_score_gemma":0.0004220806,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009420145,"about_ca_topic_score_gemma":0.000120082,"domain_scores_codex":[0.9970479,0.0002036861,0.0006748887,0.0008833156,0.0003860201,0.0008042],"domain_scores_gemma":[0.9987623,0.0000767954,0.0000963112,0.0006177094,0.00008445889,0.0003624276],"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":[0.003259386,0.0003480026,0.01720759,0.00004348202,0.005474505,0.001381709,0.003826901,0.00009748584,0.9651066,0.001154753,0.00165548,0.0004440929],"study_design_scores_gemma":[0.005111011,0.0007272857,0.01800674,0.00006070712,0.0004758552,0.0006070045,0.005138181,0.003489011,0.9655899,0.00002954221,0.0003265974,0.0004382173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928221,0.001630127,0.0008106826,0.0008549977,0.000305591,0.001406361,0.00002406183,0.00008881006,0.002057326],"genre_scores_gemma":[0.9964831,0.00004550712,0.001476875,0.0005014004,0.00003469009,0.0001602243,0.0002151006,0.00005570622,0.001027421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00499865,"threshold_uncertainty_score":0.9998968,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213253589279873,"score_gpt":0.3434443823308263,"score_spread":0.3313118464380276,"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."}}