{"id":"W2345617480","doi":"10.1261/rna.055392.115","title":"Comparative genomic analysis of upstream miRNA regulatory motifs in <i>Caenorhabditis</i>","year":2016,"lang":"en","type":"article","venue":"RNA","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Ontario Institute for Cancer Research","funders":"National Institute of General Medical Sciences; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Foundation for the National Institutes of Health","keywords":"Biology; Caenorhabditis elegans; Enhancer; Genetics; Sequence motif; microRNA; Gene; Computational biology; Caenorhabditis; Regulatory sequence; Conserved sequence; Comparative genomics; Regulation of gene expression; Genomics; Genome; Gene expression; 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":[0.0000743694,0.0001621999,0.0001653078,0.0002556944,0.0001280153,0.0001476953,0.0001400635,0.0001383216,0.000367914],"category_scores_gemma":[0.0001622822,0.0001384331,0.0002281923,0.0001078617,0.0001327967,0.00006833959,0.0001000977,0.0002817231,0.0001866757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002169642,"about_ca_system_score_gemma":0.0001588474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001187281,"about_ca_topic_score_gemma":0.002318229,"domain_scores_codex":[0.9999422,0.000005870811,0.000005066935,0.00002577856,0.00001147886,0.000009565324],"domain_scores_gemma":[0.9998381,0.00003334855,0.0000687787,0.00001065747,0.00001590071,0.00003325156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000529291,0.000005935389,0.0002935609,0.00001333144,0.000002475796,0.00002393984,0.000008172657,0.0000368128,0.9989443,0.00003145881,0.000006697399,0.0005803664],"study_design_scores_gemma":[0.00003236776,0.0004865798,0.1243029,0.00002579454,0.00008180502,0.0007680719,0.00008120192,0.002452303,0.8682857,0.0001536109,0.003306938,0.00002271162],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966682,0.0003122351,0.002065183,0.00001608585,0.000004907414,0.00001376019,0.000355909,0.00002880237,0.0005350146],"genre_scores_gemma":[0.9929054,0.0001789714,0.004952267,0.00003942756,0.000003651978,0.00002062169,0.001338029,0.00002012553,0.0005414994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001187281,"threshold_uncertainty_score":0.002360702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01069185279314056,"score_gpt":0.2380043602504492,"score_spread":0.2273125074573087,"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."}}