{"id":"W2114368649","doi":"10.1093/bioinformatics/bti1045","title":"GenXHC: a probabilistic generative model for cross-hybridization compensation in high-density genome-wide microarray data","year":2005,"lang":"en","type":"article","venue":"Computer applications in the biosciences","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative model; Computer science; Probabilistic logic; Generative grammar; Genome; Compensation (psychology); Computational biology; Statistical model; Microarray; Data mining; Artificial intelligence; Biology; Genetics; Gene; 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":[0.0005293801,0.000106061,0.00008843435,0.00007843462,0.000189422,0.00009868706,0.0008888767,0.00006002082,0.000001290092],"category_scores_gemma":[0.00003171908,0.0000823168,0.00002231056,0.0003140318,0.0001577093,0.00002370279,0.0001424514,0.00005149076,0.00000248876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003892796,"about_ca_system_score_gemma":0.0001085443,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001191145,"about_ca_topic_score_gemma":0.0002294039,"domain_scores_codex":[0.9988448,0.00006793327,0.0002537562,0.0005540453,0.0001236919,0.0001557917],"domain_scores_gemma":[0.9990366,0.0000373174,0.0001087162,0.0006978556,0.00009294049,0.00002657089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007834867,0.0008021338,0.02207671,0.0000549312,0.00001616581,1.889759e-7,0.002249743,0.410062,0.5272419,0.006708571,0.005572909,0.02513641],"study_design_scores_gemma":[0.0005077994,0.00005207564,0.07807157,0.000007800623,0.000008081193,0.000002151013,0.0001008713,0.8813123,0.02074239,0.002029917,0.01692748,0.0002375878],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3822097,0.0001005025,0.6162708,0.0006303068,0.00003952845,0.000682828,0.00003939678,0.00000674528,0.0000201681],"genre_scores_gemma":[0.9565369,0.00004759501,0.04135664,0.0005919743,0.0002167588,0.0003602795,0.0008274369,0.000006296738,0.0000560663],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5749142,"threshold_uncertainty_score":0.3356782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04346260838480358,"score_gpt":0.3150488217447869,"score_spread":0.2715862133599833,"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."}}