{"id":"W2009860000","doi":"10.1111/j.1541-0420.2007.00899.x","title":"A Flexible and Powerful Bayesian Hierarchical Model for ChIP–Chip Experiments","year":2007,"lang":"en","type":"article","venue":"Biometrics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"National Human Genome Research Institute","keywords":"Chip; Bayesian probability; Computer science; Bayesian hierarchical modeling; Hierarchical database model; Bayesian inference; Artificial intelligence; Data mining; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009404154,0.001395425,0.002992328,0.001856653,0.001486071,0.002229938,0.00602926,0.002571345,0.005049954],"category_scores_gemma":[0.01718533,0.001887754,0.002223636,0.003088724,0.002176346,0.002722979,0.001890734,0.00440737,0.00165659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002973214,"about_ca_system_score_gemma":0.003619423,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01902886,"about_ca_topic_score_gemma":0.02558205,"domain_scores_codex":[0.9959127,0.001968818,0.0001617391,0.0009349823,0.0007494768,0.0002722558],"domain_scores_gemma":[0.9917026,0.005911513,0.0005790861,0.0007257363,0.0008316527,0.0002494605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003171298,0.0001297018,0.003104853,0.0003652224,0.0002710763,0.0002737659,0.0002484643,0.769056,0.005457033,0.1619604,0.005348701,0.05346775],"study_design_scores_gemma":[0.00004906025,0.0000247879,0.0006283246,0.00001895323,0.00003987931,0.00005016461,0.00001385821,0.9379264,0.0004921646,0.0582464,0.002469505,0.00004050103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004085931,0.0002634905,0.9931693,0.0002910774,0.00003460481,0.0001440951,0.0006256782,0.0004439561,0.0009417626],"genre_scores_gemma":[0.2400416,0.001282066,0.7422491,0.0009735593,0.0002104492,0.002648259,0.003973417,0.0003837748,0.008237793],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01902886,"threshold_uncertainty_score":0.04973453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03993924728223888,"score_gpt":0.333754593863387,"score_spread":0.2938153465811482,"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."}}