{"id":"W2889750398","doi":"10.1080/02664763.2018.1517145","title":"Bayesian growth curve model useful for high-dimensional longitudinal data","year":2018,"lang":"en","type":"article","venue":"Journal of Applied Statistics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Test statistic; Computer science; Inference; Monotone polygon; Curse of dimensionality; Null hypothesis; Statistical hypothesis testing; Bayesian probability; Statistic; Singularity; Null (SQL); Mathematics; Sample size determination; Statistics; Data mining; Artificial intelligence","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":[0.0002713078,0.0001108168,0.000147711,0.0000493387,0.00009069901,0.00002459066,0.0003443085,0.00008420122,0.00002255164],"category_scores_gemma":[0.00008467609,0.00009569072,0.00002923592,0.00005074315,0.00008335745,0.000006054748,0.0001009997,0.00008216699,0.000003266067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001568841,"about_ca_system_score_gemma":0.0002280428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001182544,"about_ca_topic_score_gemma":0.000007985314,"domain_scores_codex":[0.9990597,0.0000118807,0.0003338628,0.0002296702,0.0002140349,0.0001508914],"domain_scores_gemma":[0.9987416,0.0000200488,0.0003246164,0.0003439369,0.0004692544,0.0001005822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001736361,0.000199587,0.0003318524,0.00004775958,0.0001727016,0.000003545075,0.00006402734,0.000883873,0.3521129,0.02414187,0.6125311,0.00777453],"study_design_scores_gemma":[0.009381996,0.0033863,0.007094745,0.0001017594,0.0005862658,0.0001404225,0.0002300778,0.192649,0.5530159,0.1306393,0.1013859,0.001388324],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01743267,0.00005013591,0.9811653,0.0001408163,0.0002645169,0.0001062809,0.0005786542,0.000002795895,0.0002587749],"genre_scores_gemma":[0.7561833,0.00003456949,0.2424386,0.0002001831,0.0006819134,0.000004246731,0.0003320114,0.00001693306,0.0001082133],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7387506,"threshold_uncertainty_score":0.3902154,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04115864328533659,"score_gpt":0.3080995801420303,"score_spread":0.2669409368566937,"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."}}