{"id":"W2113892406","doi":"10.1093/bioinformatics/btm491","title":"<i>CellLine</i>, a stochastic cell lineage simulator","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Lineage (genetic); Computer science; Cellular differentiation; Cell fate determination; Bistability; Gene regulatory network; Biology; Gene; Biological system; Computational biology; Genetics; Physics; Gene expression","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":[0.0004372071,0.0002037803,0.0001772722,0.00008224772,0.00008862509,0.00002882549,0.0002346764,0.0001878978,0.00002768303],"category_scores_gemma":[0.00004474264,0.0001919392,0.0001648585,0.0002034841,0.00005926108,0.000005434195,0.0001156562,0.00009088797,0.000138793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000185423,"about_ca_system_score_gemma":0.0000602341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002058264,"about_ca_topic_score_gemma":0.00001233431,"domain_scores_codex":[0.9987193,0.00001355479,0.0004779024,0.0001720085,0.0002155444,0.0004016679],"domain_scores_gemma":[0.9989994,0.00002414759,0.0001722903,0.0005170949,0.0001023984,0.0001846419],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005924909,0.0009104664,0.005771137,0.0006577333,0.0008246272,0.00003580624,0.001457809,0.5142111,0.384041,0.0002761897,0.063205,0.02801665],"study_design_scores_gemma":[0.002755576,0.0006538213,0.001170027,0.00003938327,0.0003133584,0.00004709667,0.0007654967,0.3230337,0.4955466,0.00004142737,0.1740801,0.001553303],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5468209,0.0006047121,0.4486507,0.00001746263,0.000209251,0.0002126097,0.00001340316,0.00004250241,0.003428491],"genre_scores_gemma":[0.9849575,0.00002847485,0.01269276,0.0003736551,0.0004433236,0.000002922852,0.0001344649,0.0000282231,0.001338692],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4381366,"threshold_uncertainty_score":0.7827054,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006656163944545625,"score_gpt":0.2249542220159751,"score_spread":0.2182980580714295,"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."}}