{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001476113,0.001933833,0.002074524,0.004051656,0.0006998716,0.00266114,0.002337509,0.001303544,0.06100684],"category_scores_gemma":[0.003405185,0.001555577,0.000987052,0.003514129,0.0003194886,0.00178774,0.004087585,0.001279778,0.06360355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004773547,"about_ca_system_score_gemma":0.001735109,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001837236,"about_ca_topic_score_gemma":0.003408531,"domain_scores_codex":[0.9991983,0.0001213173,0.0001212387,0.0002195457,0.0002358944,0.0001037817],"domain_scores_gemma":[0.998985,0.0003160267,0.0001286533,0.0001530958,0.0001461759,0.000271179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002870428,0.0001343004,0.006254239,0.004860582,0.0002837201,0.0005848865,0.0004162088,0.0009181864,0.03243174,0.005114251,0.7505,0.1956316],"study_design_scores_gemma":[0.0002948149,0.00008602297,0.004976653,0.0003557148,0.0001168028,0.0009412617,0.00007571119,0.001859913,0.01164321,0.004739556,0.9747859,0.000124386],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01081812,0.007856144,0.06645935,0.0005516974,0.000546583,0.0004116434,0.7573854,0.1322946,0.02367652],"genre_scores_gemma":[0.01281379,0.002690748,0.0740736,0.0006086878,0.0001568267,0.0005887316,0.8855314,0.01487627,0.008659936],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06100684,"threshold_uncertainty_score":0.2040882,"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."}}