{"id":"W4239674587","doi":"10.21203/rs.2.9587/v1","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":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières; Université du Québec","funders":"","keywords":"Gene; Transcriptome; Reference genes; Computational biology; Biology; Genetics; 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":[0.00380591,0.00146657,0.001331505,0.001405465,0.0005824166,0.001387391,0.00105623,0.0009788633,0.002961826],"category_scores_gemma":[0.005167555,0.0005381959,0.001342115,0.001837189,0.0008860236,0.000786237,0.0009753323,0.001265905,0.00157727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00111207,"about_ca_system_score_gemma":0.001017823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006456671,"about_ca_topic_score_gemma":0.001826482,"domain_scores_codex":[0.9951349,0.0007269523,0.0003425896,0.002180609,0.001367838,0.0002471214],"domain_scores_gemma":[0.9972771,0.001030162,0.0004322189,0.000397704,0.0007718252,0.00009100006],"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":[0.000482988,0.000171682,0.006040388,0.0008720313,0.0001158639,0.000167584,0.000192541,0.002573477,0.9296286,0.001224486,0.00137302,0.05715735],"study_design_scores_gemma":[0.00004616098,0.0003383859,0.01842502,0.0001094281,0.0002350689,0.0003913865,0.00009760785,0.01764377,0.9456962,0.001269142,0.01565533,0.00009258599],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2629043,0.003865503,0.7140754,0.0003011451,0.0005981576,0.0008095721,0.005169177,0.008245422,0.004031272],"genre_scores_gemma":[0.3106051,0.001068335,0.6726709,0.0004402724,0.0001071259,0.001434926,0.007556419,0.002123146,0.003993642],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00380591,"threshold_uncertainty_score":0.02012777,"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."}}