{"id":"W2804219602","doi":"10.1182/blood-2018-03-838136","title":"A gene signature that distinguishes conventional and leukemic nonnodal mantle cell lymphoma helps predict outcome","year":2018,"lang":"en","type":"article","venue":"Blood","topic":"Lymphoma Diagnosis and Treatment","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; BC Cancer Agency","funders":"European Regional Development Fund; Instituto de Salud Carlos III; National Cancer Institute; Agència de Gestió d'Ajuts Universitaris i de Recerca; National Institutes of Health; Ministerio de Economía y Competitividad; Generalitat de Catalunya; Centres de Recerca de Catalunya","keywords":"Mantle cell lymphoma; Lymphoma; Outcome (game theory); Gene signature; Signature (topology); Biology; Computational biology; Cancer research; Bioinformatics; Oncology; Gene; Medicine; Genetics; Immunology; Gene expression; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004474024,0.0003235003,0.0003233567,0.001177643,0.0001324118,0.0005021426,0.0002198821,0.0003067626,0.001868371],"category_scores_gemma":[0.001337856,0.00007662131,0.0002374595,0.0006387749,0.000296079,0.0002860157,0.0002919153,0.0003080959,0.0003404232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00033149,"about_ca_system_score_gemma":0.000246452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005646629,"about_ca_topic_score_gemma":0.001088251,"domain_scores_codex":[0.9997382,0.00005883931,0.00002653973,0.00006195017,0.00006826768,0.00004620412],"domain_scores_gemma":[0.9994555,0.0001580199,0.0001733178,0.00005365824,0.00006044108,0.00009909155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005910581,0.0000530763,0.904884,0.0000399286,0.00006895103,0.0001848761,0.00003341254,0.0005214323,0.06447631,0.0001161502,0.0003137595,0.02871694],"study_design_scores_gemma":[0.0000216349,0.0002770266,0.977078,0.0000103946,0.000101577,0.001801998,0.00006727722,0.003784996,0.01534528,0.0004493092,0.001050833,0.00001165596],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995482,0.0005542337,0.002145753,0.0001191738,0.00001330215,0.00002234333,0.0006977951,0.00006717285,0.0008982075],"genre_scores_gemma":[0.9981405,0.00006724981,0.001055089,0.00002911357,0.00001305108,0.000009456399,0.0004811167,0.000005219522,0.0001992223],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001868371,"threshold_uncertainty_score":0.006250322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01695697149926651,"score_gpt":0.2457877624041352,"score_spread":0.2288307909048687,"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."}}