{"id":"W4387500504","doi":"10.3389/fbinf.2023.1275787","title":"DeepRaccess: high-speed RNA accessibility prediction using deep learning","year":2023,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Institute of Genetics; Japan Agency for Medical Research and Development","keywords":"RNA; Computer science; Translation (biology); Artificial intelligence; Software; Feature (linguistics); Source code; Transcriptome; Deep learning; Computational biology; Nucleic acid secondary structure; Messenger RNA; Data mining; Biology; Genetics; Gene; Gene expression","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006445435,0.0001681915,0.0001978134,0.0001878091,0.0001488843,0.00007063872,0.0002768156,0.0002413453,0.0000150612],"category_scores_gemma":[0.0002459499,0.0001653056,0.00007004059,0.0003942052,0.00005328188,0.00004108625,0.000159546,0.0001500213,0.0000195953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005001123,"about_ca_system_score_gemma":0.0000454068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001461354,"about_ca_topic_score_gemma":0.000004589275,"domain_scores_codex":[0.9987001,0.00008241832,0.0004507814,0.0002181175,0.0002056005,0.0003429604],"domain_scores_gemma":[0.999361,0.0000127302,0.0001859371,0.0003165812,0.00005170448,0.00007200443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005331917,0.000160591,0.2406794,0.0006499181,0.0002994941,0.0000211635,0.002350104,0.05455767,0.3685396,0.0001358493,0.006615525,0.3254575],"study_design_scores_gemma":[0.001518226,0.0003113705,0.02263577,0.0001220071,0.00005765136,0.00001279772,0.003648543,0.6760172,0.2853942,0.002146839,0.007397094,0.0007382922],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7849553,0.0002089798,0.212815,0.00002574493,0.0009503414,0.0002751722,0.00001381591,0.00007495773,0.0006807135],"genre_scores_gemma":[0.911184,0.0005171947,0.08745673,0.00008181765,0.0001903016,0.00001757116,0.0002595199,0.00003183044,0.0002610228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6214595,"threshold_uncertainty_score":0.6740966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01519995789394489,"score_gpt":0.2543608547585818,"score_spread":0.2391608968646369,"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."}}