{"id":"W4403174248","doi":"10.1002/ctd2.70009","title":"Developing messenger RNA biomarkers: A workflow to characterise and identify transcript target sequences unaffected by alternative splicing for reproducible gene transcript quantification by reverse transcriptase quantitative polymerase chain reaction","year":2024,"lang":"en","type":"article","venue":"Clinical and Translational Discovery","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"","keywords":"Reverse transcriptase; RNA splicing; Biology; Messenger RNA; Gene; Alternative splicing; Mature messenger RNA; Computational biology; RNA; Post-transcriptional modification; Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009815483,0.0002557064,0.0003019933,0.000111988,0.0001882973,0.0002103891,0.0001113195,0.0001683864,0.000006191874],"category_scores_gemma":[0.0001462743,0.0002336443,0.0001785238,0.0002371835,0.0002026866,0.00014263,0.00001059857,0.0001753492,0.000002867308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001315388,"about_ca_system_score_gemma":0.0001291635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001327356,"about_ca_topic_score_gemma":0.00003406503,"domain_scores_codex":[0.9976061,0.0002036944,0.0005927111,0.001064494,0.0002177524,0.0003152394],"domain_scores_gemma":[0.9992514,0.0002015583,0.00007005822,0.0001886623,0.00009256141,0.000195813],"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":[0.001266136,0.00008893243,0.000363522,0.0001979033,0.0003188465,0.000004241677,0.0002682137,0.000008258077,0.9856529,0.0004944112,0.002007025,0.009329561],"study_design_scores_gemma":[0.002551715,0.0009082775,0.008842403,0.0005947018,0.0001934954,0.0000216722,0.0002969615,0.005109693,0.9548739,0.00194322,0.02380104,0.0008628769],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8082961,0.009215541,0.1749426,0.005135874,0.0002968296,0.000754393,0.001318905,0.00002219022,0.00001755301],"genre_scores_gemma":[0.988423,0.002424674,0.005252611,0.0004170308,0.0001869069,0.0001347662,0.002209863,0.00004102349,0.0009100486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1801269,"threshold_uncertainty_score":0.9527736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05534122912123656,"score_gpt":0.375574750912204,"score_spread":0.3202335217909674,"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."}}