{"id":"W4297231005","doi":"10.1093/nar/gkac835","title":"snoDB 2.0: an enhanced interactive database, specializing in human snoRNAs","year":2022,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"RNA modifications and cancer","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds de recherche du Québec – Nature et technologies; Direction Générale de l’offre de Soins; Institut National Du Cancer; Centre National de la Recherche Scientifique; Ligue Contre le Cancer; Institut National de la Santé et de la Recherche Médicale; Fonds de Recherche du Québec - Santé; Fondation ARC pour la Recherche sur le Cancer; Agence Nationale de la Recherche","keywords":"Small nucleolar RNA; Biology; Computational biology; RNA; Ribosomal RNA; Gene; Non-coding RNA; Genetics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008925936,0.00009415173,0.0001025287,0.0001771972,0.0004742995,0.00005217619,0.0005297492,0.00005114042,0.001087651],"category_scores_gemma":[0.00007257346,0.000104364,0.00003899863,0.0003658828,0.0001145897,0.00001463268,0.0005644803,0.0004879388,0.00001998089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001532135,"about_ca_system_score_gemma":0.0001297512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002776393,"about_ca_topic_score_gemma":0.0002667026,"domain_scores_codex":[0.9981567,0.0004045671,0.0001830773,0.0004760755,0.0004069853,0.0003725941],"domain_scores_gemma":[0.9991201,0.00001650969,0.00003875051,0.0006222337,0.0001169756,0.00008539016],"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":[0.0001240202,0.0001857082,0.0007722219,0.000005285538,0.00001178785,0.000007481861,0.0003599892,0.00006771496,0.9888425,0.0007569921,0.002951849,0.005914463],"study_design_scores_gemma":[0.001420415,0.001263443,0.0142165,0.00002224539,0.000005208426,0.00001287826,0.007465909,0.0003290824,0.6396396,0.0007026284,0.3344831,0.0004390139],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98127,0.0001595528,0.0002556632,0.0001417708,0.00008987626,0.0002332484,0.00005429091,0.000009348119,0.01778629],"genre_scores_gemma":[0.9967444,0.0000691001,0.0002208083,0.00009076835,0.0002836791,0.000209323,0.000358183,0.00002805574,0.001995664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3492029,"threshold_uncertainty_score":0.9998255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06478861334743438,"score_gpt":0.4001100382755938,"score_spread":0.3353214249281594,"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."}}