{"id":"W3042805293","doi":"10.1101/2020.07.14.199893","title":"Consistent Consideration of RNA Structural Alignments Improves Prediction Accuracy of RNA Secondary Structures","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics","keywords":"Pairwise comparison; Probabilistic logic; Sequence (biology); Multiple sequence alignment; Structural alignment; Protein secondary structure; Computer science; Sequence alignment; Alignment-free sequence analysis; Algorithm; Nucleic acid secondary structure; Computational biology; Mathematics; Theoretical computer science; Artificial intelligence; Biology; RNA; Genetics; Peptide sequence; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001352293,0.0005858355,0.0006343095,0.0006309983,0.0003390005,0.0007180582,0.0006612155,0.00057677,0.001019751],"category_scores_gemma":[0.004259426,0.0003155834,0.0003871069,0.0005038197,0.0003171279,0.001079665,0.0008070063,0.0007654068,0.0003726267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000248294,"about_ca_system_score_gemma":0.0007083616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001445711,"about_ca_topic_score_gemma":0.003167278,"domain_scores_codex":[0.9991673,0.0002777251,0.00003999751,0.000260257,0.0002012424,0.00005342212],"domain_scores_gemma":[0.9979203,0.001086171,0.0002437458,0.0004250701,0.0002207704,0.0001039148],"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":[0.001403895,0.0003848832,0.05115109,0.000188978,0.0002576384,0.0002259533,0.0001201877,0.547378,0.1550411,0.005544015,0.002956768,0.2353476],"study_design_scores_gemma":[0.00001606744,0.00007142825,0.003469189,0.000004819308,0.00001272128,0.00003652511,0.00001496501,0.9822605,0.01230473,0.001519394,0.0002820194,0.000007540483],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4704091,0.0004111858,0.5245931,0.0001459337,0.0000403918,0.00004031041,0.0002706466,0.002556474,0.00153281],"genre_scores_gemma":[0.8810738,0.00007527573,0.1176787,0.00004542049,0.00002078821,0.00002345973,0.0004851054,0.0001724738,0.0004249372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001445711,"threshold_uncertainty_score":0.007151723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01380231147940281,"score_gpt":0.2272477683186329,"score_spread":0.2134454568392301,"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."}}