{"id":"W2573696390","doi":"10.1093/bioinformatics/btw773","title":"The super-n-motifs model: a novel alignment-free approach for representing and comparing RNA secondary structures","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Pseudoknot; Protein secondary structure; Computer science; Adjacency list; Structural motif; Nucleic acid secondary structure; RNA; Representation (politics); Theoretical computer science; Algorithm; Computational biology; Biology","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":[0.0003310436,0.000141159,0.0001273582,0.00001937298,0.0002603349,0.00008105543,0.0003306408,0.0001033796,0.000001640912],"category_scores_gemma":[0.0001871815,0.00007995523,0.00006681409,0.00002139324,0.00008108997,0.00001416206,0.0002839961,0.00003688863,6.410523e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008220869,"about_ca_system_score_gemma":0.00003683155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002648683,"about_ca_topic_score_gemma":0.000002947225,"domain_scores_codex":[0.9991654,0.00001294484,0.0002776469,0.000160203,0.0001217151,0.0002621126],"domain_scores_gemma":[0.999217,0.00004057905,0.0001156498,0.0005207261,0.00004352615,0.0000624726],"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":[0.0001185587,0.00002419758,0.0002158667,0.0001597252,0.0001287041,1.19589e-7,0.0003229209,0.0002505183,0.9418598,0.007933156,0.003191062,0.04579531],"study_design_scores_gemma":[0.002257144,0.0001558108,0.0001056192,0.00004127893,0.00004584241,0.00003162606,0.0007171789,0.1442061,0.8340715,0.006100675,0.0118079,0.0004593082],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1517536,0.0003800974,0.8434153,0.0002064112,0.00009414597,0.0005390921,0.00007388665,0.00002142874,0.003516048],"genre_scores_gemma":[0.8342381,0.0001469868,0.1645065,0.0001325578,0.0001406454,0.00007450002,0.00003497467,0.00002656992,0.0006991876],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6824845,"threshold_uncertainty_score":0.326048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02350640389864576,"score_gpt":0.2371540220035858,"score_spread":0.2136476181049401,"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."}}