{"id":"W2617921759","doi":"10.1007/s11538-017-0356-4","title":"A Multi-stage Representation of Cell Proliferation as a Markov Process","year":2017,"lang":"en","type":"article","venue":"Bulletin of Mathematical Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"National Centre for the Replacement, Refinement and Reduction of Animals in Research; University of Bath; London Mathematical Society; Medical Research Scotland","keywords":"Markov chain; Exponential growth; Process (computing); Exponential function; Markov process; Computer science; Representation (politics); Algorithm; Variance (accounting); Cell cycle; Exponential distribution; Biological system; Applied mathematics; Mathematics; Biology; Cell; Statistics; Machine learning","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":[0.0002706636,0.0001151237,0.0002874841,0.00003444581,0.00006928481,0.000009924435,0.0002961549,0.0001821101,0.0003260087],"category_scores_gemma":[0.0006282037,0.00009862625,0.0001249579,0.00002889241,0.0002451462,0.000001295098,0.0001222037,0.00004650242,0.00003084429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003643169,"about_ca_system_score_gemma":0.00003655883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001434599,"about_ca_topic_score_gemma":0.000004357943,"domain_scores_codex":[0.9990265,0.00009244645,0.0003673198,0.000274516,0.00009157431,0.0001476183],"domain_scores_gemma":[0.9986812,0.00002918978,0.000453679,0.0006272513,0.000161503,0.00004710813],"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":[0.0001816817,0.0004220469,0.008854375,0.0003704245,0.0001206485,0.000001539712,0.0001017531,0.00006637128,0.9874555,0.0007409396,0.0007772944,0.0009073757],"study_design_scores_gemma":[0.000779459,0.0003215128,0.001560674,0.00002898953,0.00006414847,0.000006487645,0.0001042978,0.0008497632,0.9930347,0.001255142,0.001842589,0.000152256],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897597,0.0001044925,0.00559638,0.0003252697,0.00002776041,0.0002626319,0.00001031561,0.000005714737,0.003907759],"genre_scores_gemma":[0.9861812,0.00003242494,0.01128087,0.00003460703,0.00005532701,0.00003146198,0.00004228961,0.00001245313,0.00232939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007293702,"threshold_uncertainty_score":0.4021862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02362474666821864,"score_gpt":0.3172508743876384,"score_spread":0.2936261277194198,"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."}}