{"id":"W3200936978","doi":"10.1101/2021.09.15.460452","title":"RNANetMotif: identifying sequence-structure RNA network motifs in RNA-protein binding sites","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto; Compute Canada; Shandong University; Government of Ontario; Clemson University","keywords":"RNA; RNA-binding protein; Computational biology; Biology; Nucleic acid structure; RNA splicing; Binding site; 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":[0.0002398558,0.0005169824,0.0004189062,0.002025665,0.0003365754,0.0003475505,0.0004757845,0.000520999,0.002219466],"category_scores_gemma":[0.000561801,0.0002225753,0.0005150553,0.0008366972,0.0001821624,0.0003831791,0.0003776689,0.0002161884,0.0003085051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005125146,"about_ca_system_score_gemma":0.0004322891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002291035,"about_ca_topic_score_gemma":0.006146615,"domain_scores_codex":[0.9998984,0.00001907488,0.000004051728,0.00003878437,0.00002440647,0.00001522061],"domain_scores_gemma":[0.9997967,0.0000738912,0.00006141197,0.00001237068,0.00002607175,0.00002955353],"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":[0.001944836,0.0006247784,0.1334028,0.001702638,0.000772481,0.001691693,0.0003820058,0.2977714,0.3769157,0.01476794,0.02045995,0.1495638],"study_design_scores_gemma":[0.00003380406,0.0001335891,0.02044262,0.00002052493,0.00005256493,0.0002603199,0.00006327107,0.9560001,0.01524292,0.003720423,0.004002498,0.00002746225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8848274,0.0006518365,0.09825598,0.0001980533,0.00003921016,0.0001333169,0.008256064,0.005678269,0.001959773],"genre_scores_gemma":[0.8733624,0.0002109209,0.112335,0.00005184413,0.00002198644,0.0001422721,0.01270532,0.0001947701,0.000975588],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002291035,"threshold_uncertainty_score":0.007424891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02044999914379285,"score_gpt":0.2337733969700833,"score_spread":0.2133233978262905,"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."}}