{"id":"W2013411684","doi":"10.1016/j.clinbiochem.2009.07.020","title":"Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis","year":2009,"lang":"en","type":"article","venue":"Clinical Biochemistry","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":214,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; St. Michael's Hospital","funders":"Canadian Institutes of Health Research","keywords":"microRNA; Pathogenesis; Biology; Clear cell renal cell carcinoma; Renal cell carcinoma; Gene expression profiling; Microarray; Kidney cancer; Cancer; Microarray analysis techniques; Bioinformatics; Cancer research; Computational biology; Gene expression; Gene; Medicine; Pathology; Immunology; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001524514,0.0001748215,0.0002191216,0.00002553839,0.00002983117,0.00001124864,0.0001574353,0.0003031749,0.00002965569],"category_scores_gemma":[0.00006629439,0.0001545814,0.0001479604,0.00005091419,0.00009098214,0.000002915034,0.0001345674,0.0001098692,9.022559e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001227864,"about_ca_system_score_gemma":0.00007879981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001658127,"about_ca_topic_score_gemma":4.445714e-7,"domain_scores_codex":[0.9986393,0.00006836836,0.000531416,0.0004799872,0.00009126194,0.0001896],"domain_scores_gemma":[0.9992586,0.00001628665,0.0001928284,0.0003573307,0.00005925623,0.0001157488],"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":[0.0001940655,0.0002919367,0.00963721,0.00006821255,0.000007135262,0.000003215999,0.000009489811,0.000005285841,0.9880008,5.357513e-7,0.0001876915,0.001594486],"study_design_scores_gemma":[0.0009302972,0.0001463386,0.04090272,0.00005048595,0.00001357894,0.000002958843,0.00003852925,0.00006679044,0.9576146,0.00003381283,0.00004301157,0.0001568889],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982262,0.001074306,0.0003139806,0.00003074835,0.00005364165,0.0001539493,0.0000858256,0.000006019321,0.00005532374],"genre_scores_gemma":[0.9983025,0.00007319143,0.0007517571,0.0000512482,0.0002423887,0.000007048672,0.0005270433,0.00001178446,0.00003298959],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0312655,"threshold_uncertainty_score":0.6303648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346715764016692,"score_gpt":0.2651947593743899,"score_spread":0.2517276017342229,"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."}}