{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001345209,0.0001385106,0.0003114639,0.0001396374,0.00002487871,0.000006526202,0.0002119388,0.0001073361,0.00006955641],"category_scores_gemma":[0.00001511386,0.0001131545,0.0001416886,0.0001701154,0.0001274889,0.000002998332,0.00007704197,0.00004273683,0.00001420187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000027341,"about_ca_system_score_gemma":0.00004883008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007379845,"about_ca_topic_score_gemma":0.0004232814,"domain_scores_codex":[0.9990467,0.00006167919,0.000268432,0.0003052697,0.000113343,0.0002045482],"domain_scores_gemma":[0.9992954,0.00001611083,0.0001213606,0.0004455216,0.00005931778,0.00006234378],"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":[0.00002597386,0.00007511746,0.03640264,0.000007050786,0.0003302405,0.000001785328,0.0003796298,0.0008294697,0.9603961,0.00005061869,0.0005092133,0.000992159],"study_design_scores_gemma":[0.0004493559,0.0001132583,0.2604669,0.00001484664,0.0001909378,0.000001827936,0.00007293822,0.000141687,0.736599,0.0001355426,0.001614836,0.0001988924],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996055,0.0005117927,0.002287208,0.00007102261,0.00007445271,0.00009366932,0.00006116142,0.00000646117,0.0008392457],"genre_scores_gemma":[0.9985931,0.0001823176,0.00042318,0.00008333504,0.00006785171,0.000005379326,0.00003940976,0.00001397558,0.0005914645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2240642,"threshold_uncertainty_score":0.4614305,"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."}}