{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001332582,0.001730338,0.001638504,0.001605671,0.0007527697,0.0008188206,0.00205303,0.001368383,0.00284524],"category_scores_gemma":[0.003481862,0.0008182567,0.001515024,0.00104921,0.0005397733,0.001171379,0.001254937,0.001449589,0.000872154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004616386,"about_ca_system_score_gemma":0.001067886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002476614,"about_ca_topic_score_gemma":0.00610554,"domain_scores_codex":[0.9993128,0.0002202426,0.00004045535,0.0002260058,0.0001524438,0.0000480498],"domain_scores_gemma":[0.9989696,0.0006605552,0.00008791743,0.000127872,0.00009571256,0.0000583942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00197454,0.000410133,0.01815161,0.0009523323,0.001122713,0.0006535117,0.0002635976,0.353579,0.06966624,0.01285352,0.02131989,0.5190529],"study_design_scores_gemma":[0.00008700304,0.00007114091,0.0009014391,0.0000155974,0.00004727396,0.0001250598,0.00002210108,0.9827048,0.005518501,0.006244861,0.004225437,0.0000369146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06190856,0.0008407402,0.9191961,0.0002092682,0.00009274555,0.0001775237,0.002260793,0.01404069,0.001273507],"genre_scores_gemma":[0.200227,0.0003953028,0.78901,0.0003445967,0.00009970205,0.0006386432,0.006441781,0.00148194,0.001360997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00284524,"threshold_uncertainty_score":0.009518206,"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."}}