{"id":"W3200673876","doi":"10.1016/j.leukres.2021.106712","title":"Improved resolution of phenotypic subsets in human T-ALL by incorporation of RNA-seq based developmental profiling","year":2021,"lang":"en","type":"letter","venue":"Leukemia Research","topic":"Acute Lymphoblastic Leukemia research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; BC Cancer Agency","funders":"Canadian Institutes of Health Research; University of British Columbia; Terry Fox Research Institute","keywords":"Profiling (computer programming); Phenotype; Computational biology; Leukemia; Biology; Genetics; Gene; Computer science","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":[0.002457802,0.0003307709,0.000579678,0.0002714384,0.000459376,0.001165982,0.0006148114,0.00316441,0.00126339],"category_scores_gemma":[0.004783459,0.0003317277,0.0004653062,0.0002347792,0.0005419122,0.0005784243,0.0005674133,0.005679925,0.001381361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006305141,"about_ca_system_score_gemma":0.000489876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009758971,"about_ca_topic_score_gemma":0.002839272,"domain_scores_codex":[0.9989445,0.0003399571,0.00009367528,0.000141273,0.00036494,0.0001155904],"domain_scores_gemma":[0.9966653,0.002181885,0.00009946265,0.0002474531,0.0006231412,0.0001827018],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001428204,0.0002312948,0.01105321,0.000594413,0.0001216845,0.001915308,0.0005672475,0.001564792,0.2676887,0.007587043,0.4134931,0.293755],"study_design_scores_gemma":[0.0002235587,0.0005470053,0.01673143,0.0001452124,0.0002005721,0.004846993,0.0005113641,0.0135597,0.1813204,0.01472496,0.7670818,0.0001068602],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08403204,0.0215776,0.0888745,0.7381927,0.03728982,0.0004569072,0.003909087,0.002601383,0.02306598],"genre_scores_gemma":[0.3525571,0.01589607,0.1063509,0.4588902,0.02544331,0.001171061,0.003357316,0.001004056,0.03533016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00316441,"threshold_uncertainty_score":0.01299822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06967804416424138,"score_gpt":0.3472874984844252,"score_spread":0.2776094543201839,"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."}}