{"id":"W4387459850","doi":"10.3390/biom13101497","title":"Enabling mRNA Therapeutics: Current Landscape and Challenges in Manufacturing","year":2023,"lang":"en","type":"review","venue":"Biomolecules","topic":"RNA Interference and Gene Delivery","field":"Biochemistry, Genetics and Molecular Biology","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; McGill University","keywords":"Messenger RNA; Computational biology; Biology; Computer science; Bioinformatics; Gene; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001498284,0.0003321443,0.0005237192,0.0002081173,0.00003331396,0.00004403406,0.0002419341,0.0002805475,0.000003806837],"category_scores_gemma":[0.00001278224,0.00027391,0.0001736155,0.00006938446,0.00005352615,0.00000262871,0.0002195155,0.0001609205,0.00004383156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001137372,"about_ca_system_score_gemma":0.00006525079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006748298,"about_ca_topic_score_gemma":0.00005975397,"domain_scores_codex":[0.9986571,0.00008752102,0.0003210522,0.0005550006,0.00009415192,0.0002851542],"domain_scores_gemma":[0.9994784,0.00002240187,0.0001174519,0.000311345,0.0000177202,0.0000526747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005165347,0.00002000733,0.000004243923,0.004059516,0.0001006846,0.0000147901,0.00002452892,2.183107e-7,0.000193781,0.0000135825,0.00005687605,0.9955066],"study_design_scores_gemma":[0.0001218413,0.00007510964,0.00001356268,0.003517476,0.00009247114,0.0000217318,0.00008070793,0.000001932512,0.001357438,0.00002552139,0.9943519,0.0003403721],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001049654,0.9982777,0.000008884136,0.00003543183,0.0002126031,0.0002260591,0.00002779013,0.00002012112,0.0001417419],"genre_scores_gemma":[0.0002924506,0.9990699,0.00001848412,0.00001658704,0.000258206,0.0000579731,0.0001599302,0.00005466486,0.00007178967],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9951662,"threshold_uncertainty_score":0.9999713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1743224943707186,"score_gpt":0.3582739617207316,"score_spread":0.183951467350013,"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."}}