{"id":"W4285078464","doi":"10.1371/journal.pcbi.1010293","title":"RNANetMotif: Identifying sequence-structure RNA network motifs in RNA-protein binding sites","year":2022,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China; Natural Sciences and Engineering Research Council of Canada; University of Toronto; National Natural Science Foundation of China; Government of Ontario; Clemson University; National Science Foundation","keywords":"RNA; RNA-binding protein; Computational biology; Binding site; Nucleic acid structure; Biology; Protein secondary structure; Riboswitch; Nucleic acid secondary structure; Non-coding RNA; Genetics; Biochemistry; 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":[0.0002652052,0.0006014254,0.0004485507,0.001340165,0.000362687,0.000371195,0.0005855377,0.000609305,0.002270716],"category_scores_gemma":[0.0008423389,0.0003236755,0.0006419093,0.0006027553,0.0002748003,0.0005168806,0.0005118786,0.0002924226,0.0002110611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005232607,"about_ca_system_score_gemma":0.0006681583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003888488,"about_ca_topic_score_gemma":0.0110653,"domain_scores_codex":[0.9999268,0.00001710503,0.000002398454,0.0000268767,0.00001623096,0.00001065184],"domain_scores_gemma":[0.9998246,0.00008644247,0.00003669232,0.00001321564,0.00001813743,0.00002097973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009523548,0.0003291459,0.04607745,0.001165677,0.0004815637,0.0008095474,0.0003780843,0.7419389,0.07188368,0.02554316,0.01340151,0.09703895],"study_design_scores_gemma":[0.00002952639,0.00004285172,0.004028221,0.00001243184,0.00002713647,0.00009358033,0.00003099838,0.984954,0.003374574,0.005269753,0.002125188,0.00001174413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7584617,0.0006357943,0.2195405,0.0003340761,0.00005677711,0.000209199,0.008768266,0.008723888,0.003269843],"genre_scores_gemma":[0.7830836,0.0002619488,0.2058594,0.00006700629,0.00002307703,0.0002389282,0.008964125,0.0003518685,0.001150067],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003888488,"threshold_uncertainty_score":0.007731736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03635237734246165,"score_gpt":0.2959010654274702,"score_spread":0.2595486880850086,"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."}}