{"id":"W4244021263","doi":"10.21203/rs.2.9587/v3","title":"Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis","year":2019,"lang":"en","type":"preprint","venue":"Research Square","topic":"Molecular Biology Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec","funders":"","keywords":"Gene; Transcriptome; Reference genes; Computational biology; Genetics; Biology; Computer science; Gene expression","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":[0.002813757,0.001275912,0.001269433,0.001571389,0.0005415657,0.0019494,0.0009139333,0.001030238,0.003489922],"category_scores_gemma":[0.004476405,0.0004532851,0.001039095,0.003249898,0.0005813229,0.001075678,0.0009535222,0.001008176,0.001834356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007397725,"about_ca_system_score_gemma":0.001128698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00186074,"about_ca_topic_score_gemma":0.003445782,"domain_scores_codex":[0.9976108,0.0003732549,0.0001615413,0.001130283,0.0005197562,0.0002044714],"domain_scores_gemma":[0.9976555,0.00100751,0.0001726107,0.0004556924,0.0006310291,0.00007766414],"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":[0.001397624,0.000200874,0.007851179,0.001062647,0.0001977767,0.0002288852,0.000143046,0.004303264,0.9147647,0.001203005,0.001752072,0.06689488],"study_design_scores_gemma":[0.0001913587,0.0008986634,0.03749692,0.0001543227,0.001007154,0.0007653626,0.0002424632,0.03683074,0.891696,0.003542589,0.0270587,0.0001157299],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6573676,0.01016672,0.2965273,0.0005352427,0.0008932311,0.0004139969,0.01982104,0.006509413,0.007765375],"genre_scores_gemma":[0.6713952,0.004553331,0.2612118,0.0007310873,0.0002352064,0.0006606449,0.04939206,0.00272466,0.00909603],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003489922,"threshold_uncertainty_score":0.01488072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05159900088133626,"score_gpt":0.384698773593082,"score_spread":0.3330997727117457,"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."}}