{"id":"W2132863465","doi":"10.1371/journal.pcbi.1000832","title":"RNAcontext: A New Method for Learning the Sequence and Structure Binding Preferences of RNA-Binding Proteins","year":2010,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":277,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"RNA-binding protein; Computational biology; RNA; Biology; RNA splicing; RNA recognition motif; Binding site; Sequence motif; Gene expression; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002219068,0.00008956178,0.0001212331,0.0000446175,0.000154536,0.00001951636,0.0001620959,0.000112067,0.0000131519],"category_scores_gemma":[0.0004549808,0.00006122259,0.0000364844,0.00005728299,0.000116996,0.000004443768,0.0000801408,0.0001936704,6.303546e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003910722,"about_ca_system_score_gemma":0.0001323027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004533312,"about_ca_topic_score_gemma":0.00006685356,"domain_scores_codex":[0.9992869,0.00009024965,0.0001468125,0.0002277352,0.00007243583,0.0001758127],"domain_scores_gemma":[0.9994309,0.0002222279,0.0001046239,0.00007942507,0.0001073494,0.00005549557],"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":[0.00004618995,0.000006726993,0.003025328,0.00001758757,0.00005478338,1.485658e-7,0.0000612299,0.0004088955,0.9818877,0.001958341,0.00002654506,0.01250654],"study_design_scores_gemma":[0.00100716,0.001023934,0.002276328,0.00003596696,0.00002783089,0.00004116508,0.0001963962,0.03215164,0.9443949,0.0165572,0.002030328,0.0002570895],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603171,0.00008701324,0.03882897,0.0003262844,0.00004473954,0.000305877,0.00004581458,0.000005896567,0.00003828912],"genre_scores_gemma":[0.939115,0.000009246215,0.06032548,0.00003696819,0.0001298847,0.00001904138,0.0001479798,0.000007671107,0.0002086649],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03749271,"threshold_uncertainty_score":0.2496585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03465608672667169,"score_gpt":0.3421777079542289,"score_spread":0.3075216212275572,"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."}}