{"id":"W2146940435","doi":"10.1261/rna.2017210","title":"Predicting in vivo binding sites of RNA-binding proteins using mRNA secondary structure","year":2010,"lang":"en","type":"article","venue":"RNA","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":198,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; University of Toronto","funders":"Canadian Institutes of Health Research; University of Toronto","keywords":"Biology; RNA-binding protein; In silico; RNA; Computational biology; Binding site; Messenger RNA; Sequence (biology); Sequence motif; Plasma protein binding; Genetics; Cell biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002612022,0.0001254688,0.0001523933,0.0001310211,0.00009655547,0.00002939446,0.0001951586,0.0001939947,0.0001060806],"category_scores_gemma":[0.0001968867,0.0001192213,0.00005808951,0.0001535358,0.00005915216,0.00001138571,0.0001348527,0.0003307281,0.000001119352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001762266,"about_ca_system_score_gemma":0.0001292576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001577314,"about_ca_topic_score_gemma":0.0004689469,"domain_scores_codex":[0.9989588,0.00003445475,0.0002277333,0.0002570965,0.0001813991,0.0003405111],"domain_scores_gemma":[0.9994972,0.00001474807,0.0001093319,0.0002373961,0.00005508169,0.00008620125],"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":[0.00002798497,0.0000133443,0.05727779,0.00005353225,0.00001871379,0.000007079159,0.00005946538,0.00003764944,0.9415289,0.00001503556,0.0000256185,0.0009349251],"study_design_scores_gemma":[0.0003138672,0.0000768926,0.001861182,0.00005589385,0.000004698173,0.00001694227,0.0001129519,0.001212239,0.9960043,0.00002039358,0.0001986077,0.0001220203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986243,0.00009150404,0.00004844666,0.00001891254,0.0001086466,0.000209756,0.00005554274,0.000008156762,0.0008346941],"genre_scores_gemma":[0.9972026,0.00001138899,0.002180478,0.00001408249,0.0002302325,0.000003992089,0.00002417141,0.00001981165,0.0003132403],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05541661,"threshold_uncertainty_score":0.4861702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155213526431542,"score_gpt":0.2707642325653751,"score_spread":0.2592120973010597,"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."}}