{"id":"W2774366720","doi":"10.1097/00007890-201407151-03040","title":"Microarray Gene Expression for Predicting Histo-Clinical Variables in Kidney Transplant Biopsies.","year":2014,"lang":"en","type":"article","venue":"Transplantation","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Microarray; Linear discriminant analysis; Microarray analysis techniques; Biopsy; Gene; Gene expression profiling; Cut-point; Medicine; Gene expression; Pathology; Biology; Statistics; Mathematics; 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":[0.001293027,0.0004036377,0.0004152893,0.0007358877,0.0001727499,0.0003453273,0.0001572717,0.0003931558,0.0009512804],"category_scores_gemma":[0.001634098,0.0001122847,0.0003311198,0.0008925778,0.0001447087,0.0002039887,0.0001853395,0.0005324275,0.0004912859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000281419,"about_ca_system_score_gemma":0.0002168637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009020134,"about_ca_topic_score_gemma":0.001620324,"domain_scores_codex":[0.9994266,0.0001898777,0.00002817069,0.0001183792,0.0001742623,0.00006276454],"domain_scores_gemma":[0.9994352,0.0003632057,0.00006238596,0.00003701893,0.00007082394,0.0000312679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003007994,0.0004347277,0.4954419,0.0003784999,0.0005088391,0.0002100571,0.0001882575,0.01510831,0.3759163,0.00032759,0.003339218,0.1051383],"study_design_scores_gemma":[0.00004321563,0.0009851152,0.8448109,0.00003090064,0.0001983665,0.0003794649,0.0001769294,0.08053134,0.06930364,0.0007375042,0.002763575,0.00003910605],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9612001,0.002676068,0.0257495,0.0002513158,0.00008211282,0.0001324548,0.007568722,0.0002844613,0.002055328],"genre_scores_gemma":[0.9694596,0.0005218698,0.02431814,0.00008814267,0.00002352423,0.0002162328,0.004321022,0.00001604406,0.001035348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001293027,"threshold_uncertainty_score":0.006838322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01842726890753359,"score_gpt":0.2819426144005194,"score_spread":0.2635153454929858,"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."}}