{"id":"W4392016073","doi":"10.1038/s41418-024-01268-8","title":"CaSSiDI: novel single-cell “Cluster Similarity Scoring and Distinction Index” reveals critical functions for PirB and context-dependent Cebpb repression","year":2024,"lang":"en","type":"article","venue":"Cell Death and Differentiation","topic":"Immune cells in cancer","field":"Immunology and Microbiology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto General Hospital; University of Toronto; Princess Margaret Cancer Centre; Ted Rogers Centre for Heart Research; University Health Network","funders":"CIHR Skin Research Training Centre; Canadian Institutes of Health Research; Government of Canada","keywords":"Computational biology; Cluster analysis; Regulator; Computer science; Biology; Cell biology; Bioinformatics; Genetics; Artificial intelligence; Gene","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":[0.0001920092,0.0001902781,0.0002189304,0.00009534869,0.0003638794,0.0001338356,0.00004063019,0.0002765317,0.00006639109],"category_scores_gemma":[0.0001039877,0.0001693874,0.00005412448,0.00004334883,0.0001121181,0.0001945296,0.0001039228,0.0002532057,0.000007641681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005911026,"about_ca_system_score_gemma":0.0000208945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000448972,"about_ca_topic_score_gemma":0.00001919556,"domain_scores_codex":[0.9988575,0.00007012489,0.0002726186,0.0004988343,0.00004521008,0.0002557252],"domain_scores_gemma":[0.9992267,0.0004353085,0.00005882633,0.0001685032,0.00006765385,0.00004300835],"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":[0.0003959594,0.0003068282,0.01323889,0.001122931,0.00008137087,0.00000223656,0.0005211883,0.000007216447,0.9579086,0.00124785,0.001166074,0.02400083],"study_design_scores_gemma":[0.01926089,0.002086295,0.2129892,0.00178917,0.002754353,0.0004150662,0.002795023,0.0119402,0.6796473,0.007656351,0.05619555,0.002470652],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8620785,0.02478988,0.1087193,0.0002434856,0.002653533,0.0005325575,0.0001048517,0.0001022606,0.0007755414],"genre_scores_gemma":[0.9967479,0.0002649954,0.00009666563,0.00007189087,0.00008715902,0.00005949088,0.0001574014,0.00002260818,0.002491914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2782613,"threshold_uncertainty_score":0.6907417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03015768586640001,"score_gpt":0.2640234845638229,"score_spread":0.2338657986974229,"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."}}