{"id":"W1965086044","doi":"10.1111/j.1523-1755.2005.00738.x","title":"Transcriptional analysis of the molecular basis of human kidney aging using cDNA microarray profiling","year":2005,"lang":"en","type":"article","venue":"Kidney International","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":96,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"U.S. Public Health Service","keywords":"Gene expression profiling; Kidney; Biology; Microarray; Gene expression; Glomerulosclerosis; Microarray analysis techniques; Complementary DNA; Real-time polymerase chain reaction; Pathology; Significance analysis of microarrays; Gene; Reverse transcription polymerase chain reaction; Kidney disease; Medicine; Endocrinology; Genetics; Proteinuria","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":[0.0001997042,0.0001593085,0.0002982688,0.0004225361,0.0002149275,0.0003585498,0.0001262945,0.0001511307,0.001025038],"category_scores_gemma":[0.0001720043,0.0001103192,0.0003180538,0.0004269815,0.0001487467,0.0001551603,0.00009391107,0.0004566956,0.0003455048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003031381,"about_ca_system_score_gemma":0.0003196491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001276642,"about_ca_topic_score_gemma":0.001630527,"domain_scores_codex":[0.9998949,0.00001246652,0.000005916199,0.00002781935,0.00003028344,0.00002856229],"domain_scores_gemma":[0.9999139,0.00002449948,0.00001231987,0.000009029655,0.00002848977,0.00001173733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001533036,0.00001925745,0.001012434,0.00002124756,0.000006268278,0.00003006736,0.00002293621,0.00006835705,0.9963666,0.0001476911,0.00007578864,0.002075947],"study_design_scores_gemma":[0.00001805828,0.000301956,0.1178925,0.000009227973,0.00007831094,0.0004397421,0.0001462952,0.002830728,0.8712605,0.0003973765,0.00660967,0.00001547173],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796034,0.00294293,0.01105432,0.000293468,0.00007433561,0.00006030086,0.003613913,0.0001174829,0.002239838],"genre_scores_gemma":[0.9764213,0.00269811,0.01009974,0.0001193267,0.00004527185,0.00009183778,0.005473501,0.00003209808,0.0050189],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001276642,"threshold_uncertainty_score":0.003429115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643561717667259,"score_gpt":0.2930152718379264,"score_spread":0.2765796546612538,"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."}}