{"id":"W4415713040","doi":"10.1158/0008-5472.can-25-0881","title":"Multiomics Profiling of T-cell Leukemia and Lymphoma Enables Targeted Therapeutic Discovery","year":2025,"lang":"en","type":"article","venue":"Cancer Research","topic":"T-cell and Retrovirus Studies","field":"Immunology and Microbiology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Vector Institute; Structural Genomics Consortium; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Institute of Genetics; Helsinki Institute of Life Science, Helsingin Yliopisto; Break Through Cancer; Biocenter Finland; Universität Wien; European Commission; Helsingin Yliopisto; Pfizer; Incyte; American Association for Cancer Research; Amgen; Austrian Science Fund; Syöpäsäätiö; Medizinische Universität Wien; BeiGene; China Scholarship Council; Québec Consortium for Drug Discovery; Norges Forskningsråd; Academy of Finland; Kreftforeningen; Gilead Sciences; Bristol-Myers Squibb; Sigrid Juséliuksen Säätiö; Signe ja Ane Gyllenbergin Säätiö","keywords":"Lymphoma; Leukemia; Profiling (computer programming); Drug; Biomarker discovery; Drug discovery","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009496065,0.0003666187,0.0006078772,0.001672492,0.0003031482,0.001386677,0.0003624646,0.0003875066,0.001691457],"category_scores_gemma":[0.001375142,0.0001916755,0.0005670502,0.001776622,0.0002197015,0.0008942896,0.001059915,0.0007338854,0.0006230438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005488257,"about_ca_system_score_gemma":0.0007568499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349228,"about_ca_topic_score_gemma":0.002442305,"domain_scores_codex":[0.9993265,0.0001349502,0.00005010085,0.0001775118,0.0002378066,0.00007310544],"domain_scores_gemma":[0.9993619,0.0001481091,0.0001781222,0.0001071335,0.0001441822,0.00006063632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008742831,0.0002317543,0.07250226,0.0007890667,0.0004316148,0.0004288815,0.0002618725,0.006520658,0.7792832,0.002918605,0.008679531,0.1270783],"study_design_scores_gemma":[0.0001146907,0.001123319,0.2776212,0.0003691329,0.0009420028,0.001911262,0.0007439486,0.03981927,0.5259054,0.01365561,0.1376027,0.0001914633],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8253728,0.01161842,0.06553858,0.002125222,0.0001191761,0.0003061261,0.08040695,0.002649999,0.01186274],"genre_scores_gemma":[0.8992682,0.005409013,0.04584972,0.0009107433,0.0000755338,0.0002464317,0.04577192,0.000389231,0.002079154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001691457,"threshold_uncertainty_score":0.005658507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298049622556849,"score_gpt":0.3294122974688488,"score_spread":0.2964318012432803,"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."}}