{"id":"W4210673729","doi":"10.1093/bioadv/vbab044","title":"From pairwise to multiple spliced alignment","year":2022,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"RNA Research and Splicing","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"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":"Pairwise comparison; RNA splicing; Gene; Computer science; Computational biology; Alignment-free sequence analysis; Gene family; Gene Annotation; Heuristic; Context (archaeology); Annotation; Gene prediction; Genome; Genetics; Biology; Sequence alignment; Artificial intelligence; RNA; Peptide sequence","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":[0.003186053,0.002332221,0.001573808,0.003041289,0.001315271,0.002488064,0.003500081,0.00194556,0.01479196],"category_scores_gemma":[0.01526651,0.001135407,0.001852799,0.005501601,0.001575696,0.005667357,0.00524468,0.003609798,0.01194178],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001027853,"about_ca_system_score_gemma":0.00169858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009432979,"about_ca_topic_score_gemma":0.001602993,"domain_scores_codex":[0.9949514,0.001699024,0.000509337,0.001424713,0.001230632,0.000184972],"domain_scores_gemma":[0.9943996,0.002302676,0.0005485609,0.001405038,0.001106201,0.0002377986],"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":[0.000374727,0.0001787807,0.003001233,0.002221612,0.0002595717,0.0006265247,0.00057197,0.07331861,0.0143904,0.1902388,0.06264161,0.6521761],"study_design_scores_gemma":[0.0000715041,0.0001265445,0.0006194878,0.000250336,0.00006068995,0.000906173,0.0002188018,0.2781536,0.01442354,0.6314972,0.07359052,0.00008165449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002797267,0.0006070846,0.9872652,0.0003641861,0.0002147884,0.0001059995,0.001138381,0.005078015,0.002429042],"genre_scores_gemma":[0.02878523,0.0004757555,0.9637349,0.0002000175,0.0001737317,0.0002239101,0.003299015,0.001918599,0.001188827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01479196,"threshold_uncertainty_score":0.04948407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044655336417397,"score_gpt":0.2654293748247018,"score_spread":0.2549828214605278,"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."}}