{"id":"W2979751848","doi":"10.1093/nar/gkz884","title":"snoDB: an interactive database of human snoRNA sequences, abundance and interactions","year":2019,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":136,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Small nucleolar RNA; Abundance (ecology); Computational biology; Genetics; Evolutionary biology; RNA; Gene; Long non-coding RNA; Ecology","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":[0.001131747,0.001997852,0.001820322,0.005544083,0.0005922969,0.002373716,0.002113598,0.001155678,0.02749697],"category_scores_gemma":[0.003809069,0.0008793041,0.0007714469,0.004767786,0.0003224387,0.0019554,0.003362376,0.001174178,0.01817865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005115153,"about_ca_system_score_gemma":0.001579229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002570396,"about_ca_topic_score_gemma":0.003945274,"domain_scores_codex":[0.9993606,0.0001041208,0.0001192967,0.0001755934,0.0001760963,0.00006431872],"domain_scores_gemma":[0.998964,0.0003910378,0.000135689,0.0001623132,0.0001325298,0.0002144791],"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":[0.003801455,0.0003167595,0.01306767,0.01283585,0.0008385484,0.001425395,0.001179516,0.003465702,0.03214344,0.009992141,0.6613637,0.2595699],"study_design_scores_gemma":[0.0004517561,0.0001456905,0.01003961,0.0009127099,0.0002981367,0.001800803,0.0003667962,0.009762771,0.01948117,0.01160989,0.9448981,0.0002325062],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"software","genre_scores_codex":[0.01307318,0.00639402,0.04489099,0.0003757791,0.0001771088,0.0002731633,0.8362957,0.08892457,0.009595416],"genre_scores_gemma":[0.02775782,0.003248453,0.0550306,0.0003636463,0.00006094918,0.0005729176,0.903934,0.006282682,0.002749014],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02749697,"threshold_uncertainty_score":0.09198654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0375488036975342,"score_gpt":0.3878909243684595,"score_spread":0.3503421206709252,"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."}}