{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003056788,0.0001001805,0.0002151572,0.0001754527,0.0002807922,0.00001730297,0.0001633642,0.0001402283,0.00003384526],"category_scores_gemma":[0.00004294098,0.0000750985,0.00004075125,0.0002586287,0.0006464467,0.00005834414,0.0002090841,0.000213897,0.0000124613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001609738,"about_ca_system_score_gemma":0.0004427346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001057304,"about_ca_topic_score_gemma":0.000102959,"domain_scores_codex":[0.9990125,0.000164164,0.0001695674,0.000242491,0.00004712254,0.0003641065],"domain_scores_gemma":[0.9994038,0.0002382193,0.0000370343,0.0001714193,0.0001381682,0.00001141056],"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":[0.0002339435,0.00005162348,0.006249968,0.0001962488,0.0001634505,0.000001151058,0.0003104562,0.000007267751,0.9824777,0.001182437,0.0006841102,0.008441648],"study_design_scores_gemma":[0.001268509,0.00006568142,0.003584821,0.00007005491,0.00002014998,8.9606e-7,0.001091881,0.00001192873,0.9864634,0.0003608533,0.006981413,0.00008039801],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9223399,0.07484777,0.00007139759,0.0001252811,0.0003046425,0.0002407162,0.00003896556,0.0000172354,0.002014051],"genre_scores_gemma":[0.9796866,0.005760659,0.00003880852,0.00004341242,0.0000184316,0.00006333858,0.00001112429,0.000008995788,0.01436864],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06908711,"threshold_uncertainty_score":0.3062428,"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."}}