{"id":"W4281297170","doi":"10.1038/s41467-022-30545-8","title":"Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":126,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ligue Contre le Cancer; Fondation Schlumberger pour l’Education et la Recherche; Université de Strasbourg; Institute of Genetics; Conseil National de la Recherche Scientifique; Agence Nationale de la Recherche","keywords":"Cell cycle; Somatic cell; Biology; RNA; Computational biology; Gene; Embryonic stem cell; Transcriptome; Cell; Gene regulatory network; RNA-Seq; Gene expression; Cell biology; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001668789,0.0001668868,0.0001840504,0.0003799081,0.000119926,0.0003331047,0.0002727633,0.0002437722,0.0005413572],"category_scores_gemma":[0.0006348072,0.0001806184,0.0001938053,0.000305406,0.000306753,0.0003256658,0.0002442915,0.0004110535,0.0001401817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005195413,"about_ca_system_score_gemma":0.0003755046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003447522,"about_ca_topic_score_gemma":0.004004342,"domain_scores_codex":[0.9999543,0.000005542951,0.000001409412,0.00001534524,0.00001223973,0.00001108999],"domain_scores_gemma":[0.9998668,0.00005003375,0.00002627186,0.00001713042,0.00002243712,0.00001738597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002633343,0.00008803852,0.02152534,0.0001053786,0.00005961024,0.0001454285,0.0001612897,0.4001309,0.4661022,0.01343938,0.001262967,0.09671617],"study_design_scores_gemma":[0.000003333832,0.00001411564,0.006394776,0.000003221734,0.00000442884,0.00001520862,0.00001072295,0.972178,0.01710427,0.003699167,0.0005648105,0.000007936957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8016514,0.000599391,0.1938799,0.000281905,0.00003315694,0.00001610345,0.0003953661,0.0006333318,0.002509548],"genre_scores_gemma":[0.9829915,0.000190592,0.01550034,0.00004036158,0.000006372247,0.0000138514,0.000249552,0.00005296159,0.0009544471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003447522,"threshold_uncertainty_score":0.006854951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006092711251101925,"score_gpt":0.2267317560524843,"score_spread":0.2206390448013824,"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."}}