{"id":"W2090537645","doi":"10.2202/1544-6115.1436","title":"A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers","year":2009,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"National Cancer Institute","keywords":"Computational biology; Multivariate statistics; Gene expression profiling; Bayesian probability; Computer science; Biology; Bioinformatics; Gene expression; Data mining; Gene; Artificial intelligence; Machine learning; Genetics","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":[0.03489269,0.001937941,0.002471334,0.0037768,0.001407863,0.002719781,0.002994867,0.001969632,0.007470665],"category_scores_gemma":[0.09201027,0.001706003,0.003217423,0.003338105,0.001616272,0.001730723,0.003396325,0.003825314,0.001919125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001672583,"about_ca_system_score_gemma":0.00366297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004939351,"about_ca_topic_score_gemma":0.004893587,"domain_scores_codex":[0.9833989,0.01185483,0.0007350205,0.002030127,0.001681082,0.0003000051],"domain_scores_gemma":[0.951582,0.03621903,0.002471125,0.005659057,0.003412401,0.0006564436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006930917,0.0003403542,0.009461475,0.0007113648,0.002504847,0.001044291,0.0006261786,0.1273845,0.01249503,0.2639925,0.009133821,0.5716125],"study_design_scores_gemma":[0.000176419,0.0003289819,0.003154251,0.0001160172,0.0005280302,0.0003588689,0.00007842287,0.5858594,0.004091041,0.3908517,0.01433089,0.0001260572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006535786,0.00009677184,0.9985783,0.0001120568,0.00002218649,0.00006071655,0.00007133889,0.0001747154,0.0002303307],"genre_scores_gemma":[0.08079121,0.0004657247,0.9127375,0.0005552341,0.0002577256,0.001756948,0.0009993032,0.0005304372,0.001905875],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03489269,"threshold_uncertainty_score":0.1845324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01668035352172421,"score_gpt":0.3585117762264449,"score_spread":0.3418314227047207,"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."}}