{"id":"W2071331838","doi":"10.1016/j.ab.2005.07.014","title":"The optimization of quantitative reverse transcription PCR for verification of cDNA microarray data","year":2005,"lang":"en","type":"article","venue":"Analytical Biochemistry","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Laurentian University; Northeast Cancer Centre","funders":"Cancer Care Ontario","keywords":"Complementary DNA; Biology; TaqMan; Molecular biology; Gene expression; Microarray; Gene; Microarray analysis techniques; Reverse transcriptase; RNA extraction; Gene expression profiling; Real-time polymerase chain reaction; RNA; Computational biology; 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":[0.0002525779,0.00008564883,0.0001062591,0.00001243533,0.00006188824,0.000005808558,0.0003495614,0.0001485685,0.000006989794],"category_scores_gemma":[0.0001776171,0.00007019928,0.00008051262,0.00008146939,0.0002247862,0.000003837054,0.00004778898,0.00004474788,6.184566e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007948614,"about_ca_system_score_gemma":0.00004990232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004815149,"about_ca_topic_score_gemma":0.000003311325,"domain_scores_codex":[0.9992349,0.00002078257,0.0002880727,0.0002820353,0.00006506885,0.0001091013],"domain_scores_gemma":[0.998924,0.00002932623,0.0001417966,0.0007056487,0.0001681709,0.00003111055],"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":[0.00008568754,0.00006265228,0.00002960569,0.00002374363,0.00004779683,2.037538e-8,0.000005452328,0.0002185035,0.9931302,0.00142304,0.003533478,0.001439841],"study_design_scores_gemma":[0.0001905851,0.00008258598,0.00002498821,0.000008860706,0.00005306973,0.000001378743,0.00004634501,0.01199265,0.9597297,0.00005067843,0.02774085,0.00007835163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1746234,0.001328564,0.8189789,0.002985903,0.00002928366,0.00056851,0.0006203559,0.00001797118,0.0008470492],"genre_scores_gemma":[0.9433206,0.0004618223,0.05367455,0.0000832986,0.00006309881,0.00003449752,0.002055927,0.00001115059,0.0002951023],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7686971,"threshold_uncertainty_score":0.2862644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03112238855252468,"score_gpt":0.3229302858270722,"score_spread":0.2918078972745475,"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."}}