{"id":"W3028851061","doi":"10.1093/nar/gkaa437","title":"CReSCENT: CanceR Single Cell ExpressioN Toolkit","year":2020,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Children’s Health Research Institute; Ontario Institute for Cancer Research; Western University; Hospital for Sick Children; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Ontario Genomics Institute; Leukemia and Lymphoma Society; Genome Canada; Ontario Genomics; Princess Margaret Cancer Foundation; Government of Canada; Ontario Institute for Cancer Research","keywords":"Biology; Computational biology; Cancer; Expression (computer science); Genetics; Evolutionary biology; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002032333,0.0001383668,0.0001323107,0.00004002306,0.0001875273,0.00007215905,0.0004288491,0.000193999,0.0003739653],"category_scores_gemma":[0.00009869878,0.000127822,0.00008420247,0.000198867,0.0001377223,0.000006483683,0.0002236394,0.000330492,0.0001094401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003075276,"about_ca_system_score_gemma":0.000104504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006183941,"about_ca_topic_score_gemma":0.00001367158,"domain_scores_codex":[0.9983314,0.0001351242,0.0001698971,0.0004685553,0.0004209524,0.0004740364],"domain_scores_gemma":[0.9992245,0.00001710682,0.0000271982,0.0003024278,0.0001704294,0.0002583149],"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":[0.0002366428,0.0001297355,0.003165906,0.00005357861,0.00000896562,0.000006621069,0.0002199493,0.00002342878,0.9755283,0.000008521411,0.0164941,0.004124251],"study_design_scores_gemma":[0.0004994786,0.0003814054,0.0003666335,0.0000192179,0.000003439145,7.538772e-7,0.00009731829,0.0001850085,0.7741499,0.00001240336,0.224149,0.000135426],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853953,0.001338429,0.0006520094,0.001344196,0.0001454176,0.0002318336,0.00002955344,0.00003121587,0.01083203],"genre_scores_gemma":[0.9962591,0.0003352682,0.000537031,0.0006196043,0.000697562,0.00002274656,0.00004028222,0.00004581729,0.001442611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2076549,"threshold_uncertainty_score":0.521243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07408769249949063,"score_gpt":0.3192408678604278,"score_spread":0.2451531753609372,"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."}}