{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005630623,0.0008629601,0.001402249,0.001015631,0.0007801946,0.0009205641,0.003267631,0.001750481,0.001801241],"category_scores_gemma":[0.007864643,0.001140931,0.001654987,0.001059531,0.001661939,0.001030471,0.001741124,0.00245606,0.0004672995],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001574671,"about_ca_system_score_gemma":0.001552133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01029765,"about_ca_topic_score_gemma":0.01271637,"domain_scores_codex":[0.9983277,0.0008136251,0.0000576724,0.0003989633,0.0003035781,0.00009843066],"domain_scores_gemma":[0.9957454,0.003305495,0.0002372124,0.0003081783,0.0003069268,0.00009688201],"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":[0.0001044779,0.00004470145,0.001629001,0.00005452171,0.0001163702,0.0000735011,0.00009971716,0.9072905,0.003148962,0.01202096,0.00189452,0.07352285],"study_design_scores_gemma":[0.000007455482,0.000006260102,0.0001496205,0.000002257941,0.000004985308,0.00001760162,0.000003301876,0.9952996,0.0004679047,0.003801014,0.0002345757,0.000005476993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00544987,0.00008629898,0.9935172,0.0001324119,0.00001399627,0.00003527667,0.00007393866,0.0005529206,0.0001381675],"genre_scores_gemma":[0.25464,0.0002152901,0.7388949,0.0006244127,0.0001232134,0.0005179847,0.001557913,0.0005052006,0.002921069],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01029765,"threshold_uncertainty_score":0.02977794,"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."}}