{"id":"W4407901728","doi":"10.1016/j.jbc.2025.108340","title":"Profiling the regulatory landscape of sialylation through miRNA targeting of CMP- sialic acid synthetase","year":2025,"lang":"en","type":"article","venue":"Journal of Biological Chemistry","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canada Excellence Research Chairs, Government of Canada; Canada Foundation for Innovation; University of Alberta","keywords":"Sialic acid; Profiling (computer programming); microRNA; Computational biology; Chemistry; Biochemistry; Biology; Computer science; Gene","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":[0.0002545206,0.0002276681,0.0003274416,0.000237935,0.0001532551,0.0005569078,0.00015514,0.0002644498,0.0007932698],"category_scores_gemma":[0.0003835584,0.0002088005,0.0003124677,0.0001705777,0.0001671434,0.0001657583,0.0002082096,0.0005721764,0.0004695191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002585989,"about_ca_system_score_gemma":0.0002909575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004384268,"about_ca_topic_score_gemma":0.0008134824,"domain_scores_codex":[0.9997303,0.0000284894,0.00001412069,0.0001022356,0.00008482576,0.00004000181],"domain_scores_gemma":[0.9998224,0.00005498542,0.00004837006,0.00001560904,0.0000347483,0.00002382531],"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":[0.00008355787,0.000008419118,0.000739593,0.00002466638,0.000006747452,0.00001404282,0.0000196099,0.0001623104,0.9968063,0.00008866428,0.00003779493,0.002008357],"study_design_scores_gemma":[0.00001406358,0.0002216815,0.01586561,0.000006948672,0.0000415715,0.0001347478,0.00004030462,0.006695452,0.9734972,0.0001530562,0.003314611,0.00001473209],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9752712,0.001742161,0.01822152,0.00011201,0.000041574,0.00007184438,0.001732837,0.0002420818,0.002564672],"genre_scores_gemma":[0.9728425,0.001198857,0.02004893,0.0001706936,0.00001767806,0.0001207498,0.001882151,0.00009867478,0.003619815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007932698,"threshold_uncertainty_score":0.002653778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787016567379077,"score_gpt":0.2907452123936104,"score_spread":0.2728750467198197,"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."}}