{"id":"W4283073988","doi":"10.1101/2022.06.17.496559","title":"LinAliFold and CentroidLinAliFold: Fast RNA consensus secondary structure prediction for aligned sequences using beam search methods","year":2022,"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 Medical Science, University of Tokyo; Japan Society for the Promotion of Science; Institute of Genetics; University of Tokyo","keywords":"Protein secondary structure; Nucleic acid secondary structure; RNA; Source code; Computer science; Benchmark (surveying); Sequence (biology); Coding (social sciences); Algorithm; Multiple sequence alignment; Computational biology; Sequence alignment; Biology; Mathematics; Genetics; Statistics; Peptide sequence","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":[0.004115601,0.001899181,0.001656349,0.001466717,0.00102961,0.001177912,0.002831102,0.001626567,0.01337783],"category_scores_gemma":[0.005774191,0.001177684,0.001542892,0.001187066,0.0006505012,0.001730543,0.001427032,0.002365685,0.005767611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007864551,"about_ca_system_score_gemma":0.001713153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002135483,"about_ca_topic_score_gemma":0.003008115,"domain_scores_codex":[0.9985167,0.0006098331,0.0001003004,0.0003158244,0.0003531332,0.0001040861],"domain_scores_gemma":[0.9977204,0.001239466,0.0001751471,0.0003432823,0.0003904583,0.0001311795],"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":[0.003786639,0.0007061037,0.01105504,0.002461191,0.001551336,0.0007666288,0.0007679862,0.1987889,0.1464985,0.03873867,0.1781213,0.4167576],"study_design_scores_gemma":[0.0004008736,0.000139949,0.000803376,0.00005327698,0.00005289334,0.0001210766,0.00003666332,0.9360906,0.04160014,0.008358203,0.01225131,0.00009164339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02131676,0.0006329339,0.8906791,0.000207428,0.0001880013,0.0002068102,0.002785124,0.08254962,0.001434243],"genre_scores_gemma":[0.07244464,0.0002092815,0.911768,0.000199627,0.00004901284,0.0006100992,0.005109484,0.008268216,0.001341688],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01337783,"threshold_uncertainty_score":0.04475325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02187931348903517,"score_gpt":0.2805114203292934,"score_spread":0.2586321068402582,"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."}}