{"id":"W4288950922","doi":"10.1016/j.molcel.2022.06.036","title":"Systematic exploration of dynamic splicing networks reveals conserved multistage regulators of neurogenesis","year":2022,"lang":"en","type":"article","venue":"Molecular Cell","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; University of Toronto","funders":"National Institutes of Health; University of Toronto; European Commission; Canada First Research Excellence Fund; Government of Ontario; Canadian Institutes of Health Research; Mitacs; Ontario Genomics; Genome Canada","keywords":"Neurogenesis; Biology; Alternative splicing; RNA splicing; Exon; Splicing factor; Neural stem cell; Cell biology; Genetics; Stem cell; Gene; RNA","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":[0.0004700572,0.0001311093,0.0002885077,0.00008336854,0.00008627243,0.000008570632,0.000248872,0.00005800682,0.00001227819],"category_scores_gemma":[0.00007815958,0.0001393044,0.0001579344,0.0002037388,0.00004818982,0.000004641141,0.0002152957,0.00009079077,7.38218e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001840447,"about_ca_system_score_gemma":0.0000464405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005081892,"about_ca_topic_score_gemma":0.00001215613,"domain_scores_codex":[0.9983704,0.0003930296,0.0004536102,0.000274959,0.0002847241,0.0002232907],"domain_scores_gemma":[0.9990023,0.00002678566,0.0002906023,0.0005249387,0.00008970914,0.00006563166],"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":[0.00005428629,0.00005643118,0.0002567845,0.001518145,0.00005778443,0.00001792481,0.00003224509,0.01642567,0.9814552,0.00003805848,0.00003695201,0.00005050286],"study_design_scores_gemma":[0.0006301738,0.0004348082,0.0002288746,0.0001688684,0.00008446597,0.000009776774,0.0003800361,0.02826502,0.9695479,0.00002468368,0.00003225574,0.0001931103],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769168,0.00241683,0.0199541,0.00002150751,0.00005254005,0.0005091137,0.00001548306,0.000007328188,0.000106301],"genre_scores_gemma":[0.9986373,0.00005512077,0.0003799588,0.00006011827,0.000007701568,0.00007290678,0.00008373133,0.00003288738,0.0006702596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02172052,"threshold_uncertainty_score":0.5680668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009388622461572226,"score_gpt":0.2390231089349369,"score_spread":0.2296344864733647,"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."}}