{"id":"W2037497419","doi":"10.1371/journal.pone.0082144","title":"A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Breast Cancer Treatment Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; Alberta Cancer Foundation; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Cancer Foundation","keywords":"Estrogen receptor; Classifier (UML); Computational biology; Gene expression; Estrogen receptor alpha; Bioinformatics; Estrogen receptor beta; Artificial intelligence; Computer science; Biology; Gene; Genetics; Breast cancer","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001903519,0.001179196,0.001127587,0.001556629,0.0005173282,0.001129825,0.0009834076,0.001577128,0.001249306],"category_scores_gemma":[0.005992304,0.000230801,0.000791369,0.0008469102,0.0003777538,0.0008028104,0.0004289933,0.001438938,0.001046061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000812444,"about_ca_system_score_gemma":0.00116022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001599418,"about_ca_topic_score_gemma":0.001469651,"domain_scores_codex":[0.9988968,0.0002219567,0.0001029302,0.0003806421,0.000292806,0.0001048279],"domain_scores_gemma":[0.9978182,0.001259114,0.0001454149,0.0001542353,0.0005716756,0.00005141527],"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":[0.0005311882,0.001263599,0.03112994,0.0003294776,0.0004997805,0.0005149153,0.0001210443,0.1763918,0.04928116,0.001620841,0.01073664,0.7275795],"study_design_scores_gemma":[0.000039016,0.0002202716,0.002974927,0.00002998409,0.00007701533,0.0001620586,0.00002262211,0.9831343,0.01030361,0.001651178,0.001360154,0.00002489759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2032209,0.0009308401,0.7858022,0.0009183781,0.0003651373,0.0005174525,0.002096626,0.003665534,0.00248301],"genre_scores_gemma":[0.6479498,0.0003724854,0.3436802,0.0005846961,0.0002109001,0.0008592419,0.003933725,0.00008901604,0.002320048],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001903519,"threshold_uncertainty_score":0.01006687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1674274134606361,"score_gpt":0.3012646022450004,"score_spread":0.1338371887843643,"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."}}