{"id":"W4396558984","doi":"10.1101/2024.04.29.591679","title":"Joint distribution of nuclear and cytoplasmic mRNA levels in stochastic models of gene expression: analytical results and parameter inference","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Inference; Expression (computer science); Gene expression; Joint probability distribution; Joint (building); Distribution (mathematics); Computational biology; Messenger RNA; Biology; Statistical physics; Econometrics; Gene; Physics; Mathematics; Computer science; Statistics; Genetics; Artificial intelligence; Engineering; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004391484,0.0003360348,0.0005868568,0.0001614003,0.00003054095,0.00004101956,0.0001850246,0.0004990026,0.00000246225],"category_scores_gemma":[0.0002418079,0.0003387693,0.0001191649,0.0002375426,0.0002354091,0.000007384571,0.0007692386,0.000330611,8.809676e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000464692,"about_ca_system_score_gemma":0.0002045682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001948155,"about_ca_topic_score_gemma":0.000002607836,"domain_scores_codex":[0.9978666,0.000109366,0.000690576,0.0008495764,0.0002178146,0.0002660576],"domain_scores_gemma":[0.9985122,0.00003618086,0.000324573,0.0007603902,0.0002206516,0.0001460155],"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":[0.00009880748,0.00006052542,0.0008081121,0.0003074419,0.0001694418,0.000008338973,0.00001235282,0.01381321,0.9844663,0.0002023147,0.0000494259,0.000003690697],"study_design_scores_gemma":[0.0009302954,0.0001835726,0.06474349,0.0011059,0.00039322,1.409138e-7,0.000007653255,0.1163412,0.8154245,0.00009278346,0.0000473741,0.0007298913],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928148,0.002051312,0.004160562,0.00004478828,0.0000822877,0.0002374749,0.0005912822,0.00001585967,0.000001676244],"genre_scores_gemma":[0.9977601,0.0002656495,0.00182192,0.00001142237,0.00007281959,0.00001848545,0.000003717471,0.00004429117,0.000001579754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1690419,"threshold_uncertainty_score":0.9999064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02082196733065323,"score_gpt":0.2321881377853767,"score_spread":0.2113661704547234,"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."}}