{"id":"W3006143259","doi":"10.1186/s12864-019-6426-2","title":"Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis","year":2020,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Reference genes; Biology; Gene; Genetics; Transcriptome; Computational biology; DNA microarray; Gene expression; Candidate gene; Gene expression profiling","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":[0.00009807978,0.0002283433,0.0002879368,0.00007790088,0.0000726126,0.00002418641,0.00038702,0.0003190553,0.00003501888],"category_scores_gemma":[0.00003531516,0.0002343406,0.0001273668,0.0005709602,0.0000617419,0.000003418255,0.0001140939,0.0001657552,0.00002241341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003207155,"about_ca_system_score_gemma":0.0002455107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008584801,"about_ca_topic_score_gemma":0.001649399,"domain_scores_codex":[0.9985104,0.00007730517,0.0003677774,0.0006618892,0.00007756082,0.0003050461],"domain_scores_gemma":[0.9991477,0.000009337967,0.0001042362,0.0004956523,0.000123372,0.0001197512],"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":[0.0001120917,0.00004563542,0.02087303,0.00001877033,0.0001144774,0.000001666854,0.00006439391,0.000663274,0.9762542,0.0002501017,0.0002034187,0.001398934],"study_design_scores_gemma":[0.0006358329,0.0002327956,0.03699534,0.000004185324,0.0002795196,0.000008622846,0.00006301497,0.003591561,0.8730743,0.0001148858,0.08443561,0.000564301],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9609532,0.001266577,0.03637407,0.0002603768,0.0000163993,0.0002888261,0.0001260065,0.00005434407,0.0006601402],"genre_scores_gemma":[0.9812714,0.001548629,0.01513045,0.0004945883,0.00006782371,0.0001052741,0.001169723,0.00002909711,0.0001830242],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1031799,"threshold_uncertainty_score":0.9556134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841950582247336,"score_gpt":0.2639272559162895,"score_spread":0.2355077500938161,"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."}}