{"id":"W4379791907","doi":"10.1002/advs.202205442","title":"Reliable Identification and Interpretation of Single‐Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning","year":2023,"lang":"en","type":"article","venue":"Advanced Science","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fundamental Research Funds for the Central Universities; People's Government of Jilin Province; National Natural Science Foundation of China","keywords":"Cluster analysis; Computer science; Pruning; Interpretability; Artificial intelligence; Identification (biology); Data mining; Hierarchical clustering; Autoencoder; Heuristic; Machine learning; Pattern recognition (psychology); Artificial neural network; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002359158,0.00007182996,0.00007133608,0.00008256893,0.0001204252,0.00002881571,0.0000771399,0.00004060816,3.750893e-7],"category_scores_gemma":[0.00003734927,0.00007924594,0.00002014375,0.0002592462,0.0002184956,0.00003659909,0.00002807094,0.00003010275,3.598299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001673905,"about_ca_system_score_gemma":0.0000339721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007462096,"about_ca_topic_score_gemma":0.00001291293,"domain_scores_codex":[0.9992518,0.00001643425,0.0001626219,0.0002976963,0.0001401856,0.0001312425],"domain_scores_gemma":[0.9996523,0.000006310746,0.00007738791,0.0001299983,0.0000953374,0.00003871346],"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":[0.00002743505,0.00001332754,0.0005427854,0.0000234268,0.000002159836,1.997065e-7,0.0001095389,0.01130951,0.9847783,0.00007333868,3.234139e-7,0.003119659],"study_design_scores_gemma":[0.0001912382,0.00006925425,0.008235411,0.00002205662,0.00000782962,0.000004274904,0.00005702619,0.1081035,0.8828719,0.0003404532,0.00001762772,0.00007943805],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8945556,0.0003271129,0.1048593,0.00001623768,0.00008417935,0.0000935057,0.000004474782,0.0000111106,0.00004839721],"genre_scores_gemma":[0.9945424,0.00007901528,0.005276893,0.00001764597,0.000005541907,0.00000280157,0.00003180164,0.000008119854,0.0000357387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1019064,"threshold_uncertainty_score":0.3231556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320188711269348,"score_gpt":0.2621363465243286,"score_spread":0.2489344594116351,"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."}}