{"id":"W2807745747","doi":"10.1093/bioinformatics/bty234","title":"aliFreeFold: an alignment-free approach to predict secondary structure from homologous RNA sequences","year":2018,"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; Canada Research Chairs; Université de Sherbrooke","keywords":"RNA; Computer science; Weighting; Computational biology; Sequence (biology); Sequence alignment; Set (abstract data type); Nucleic acid secondary structure; Protein secondary structure; Structural alignment; Homologous chromosome; Nucleic acid structure; Multiple sequence alignment; Algorithm; Pseudoknot; Protein superfamily; Theoretical computer science; Biology; Genetics; Peptide sequence; Physics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001797251,0.0002419086,0.0001970047,0.00005528568,0.0001530932,0.00008147221,0.0008288398,0.0002846271,0.0001443791],"category_scores_gemma":[0.00008085057,0.0002023381,0.0000654074,0.00008998616,0.0001250545,0.00002366456,0.000280718,0.00008165173,0.0000471872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001789793,"about_ca_system_score_gemma":0.0001034768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006191494,"about_ca_topic_score_gemma":0.00004002615,"domain_scores_codex":[0.9987174,0.00005661273,0.0003475896,0.000287793,0.0002528403,0.000337743],"domain_scores_gemma":[0.998528,0.000009041575,0.0001348956,0.001030628,0.00007785387,0.0002196004],"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":[0.0001381086,0.00008570536,0.0001808216,0.00005638093,0.0001782715,0.000002819693,0.001623245,0.00003060734,0.9333605,0.0005182903,0.02158477,0.04224051],"study_design_scores_gemma":[0.0005167789,0.001115307,0.0003320728,0.00001960569,0.00003679669,0.0000313064,0.0007447766,0.0006544836,0.9667415,0.002505179,0.0268595,0.0004427214],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9324899,0.0002440429,0.04380306,0.0001181261,0.0005944407,0.0006679285,0.00144562,0.00007739181,0.02055947],"genre_scores_gemma":[0.8256568,0.0000282951,0.1708126,0.001513243,0.0009174314,0.00003207393,0.0006963241,0.0000318292,0.0003114286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1270095,"threshold_uncertainty_score":0.8251109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142369341099572,"score_gpt":0.2255440498882668,"score_spread":0.214120356477271,"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."}}