{"id":"W4405953062","doi":"10.1093/bib/bbae682","title":"R3Design: deep tertiary structure-based RNA sequence design and beyond","year":2024,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"National Natural Science Foundation of China; Westlake University; Compute Canada","keywords":"Sequence (biology); Computational biology; Computer science; RNA; Biology; Genetics; Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003640962,0.002229969,0.001966127,0.0008243034,0.0009269,0.001779094,0.002064406,0.001871097,0.006248509],"category_scores_gemma":[0.004040341,0.001100407,0.002660781,0.0007543514,0.001105438,0.001447292,0.001547439,0.00324732,0.004580123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001179256,"about_ca_system_score_gemma":0.002304638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001531471,"about_ca_topic_score_gemma":0.002900654,"domain_scores_codex":[0.9981555,0.0006969487,0.0001057716,0.0004314767,0.0005036566,0.0001065494],"domain_scores_gemma":[0.9984493,0.0008603662,0.0001555589,0.0002848576,0.0001542635,0.00009558875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001061744,0.0003998654,0.003786032,0.002426986,0.0004919953,0.0005440856,0.0003289865,0.4244347,0.1464806,0.0458825,0.0373538,0.3368087],"study_design_scores_gemma":[0.0002184759,0.0004581341,0.0003097815,0.0001170494,0.00008155957,0.0002460979,0.00003861665,0.8780268,0.05343771,0.03220986,0.03476302,0.00009282641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01704009,0.002640743,0.9459487,0.0005413389,0.0002625684,0.0001919136,0.001402415,0.02638391,0.005588252],"genre_scores_gemma":[0.1143012,0.001813964,0.8694991,0.0009216029,0.00009376829,0.0005731247,0.004619033,0.004663025,0.003515154],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006248509,"threshold_uncertainty_score":0.02090335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339733021504876,"score_gpt":0.2380573515730635,"score_spread":0.2246600213580147,"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."}}