{"id":"W4404202912","doi":"10.1038/s41467-024-54134-z","title":"Non-targeted N-glycome profiling reveals multiple layers of organ-specific diversity in mice","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institutes of Health; Universität Wien; Bundesministerium für Bildung und Forschung; Austrian Science Fund; National Institute of Allergy and Infectious Diseases; EQUIPMENT BOKU VIENNA INSTITUTE OF BIOTECHNOLOGY; Universität für Bodenkultur Wien; Medizinische Universität Wien; European Commission; European Federation of Pharmaceutical Industries and Associations","keywords":"Glycome; Profiling (computer programming); Computational biology; Biology; Diversity (politics); Genetics; Computer science; Glycan; Glycoprotein","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004338273,0.0004216052,0.0003244755,0.001053821,0.0002031515,0.0005740314,0.0002517524,0.0003967344,0.0007834242],"category_scores_gemma":[0.0002300643,0.0002886265,0.0005095347,0.0004088577,0.0003824485,0.000459332,0.0004396187,0.001259442,0.0003058903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003360783,"about_ca_system_score_gemma":0.0002104691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008779718,"about_ca_topic_score_gemma":0.001540615,"domain_scores_codex":[0.9996856,0.00003101255,0.00001782774,0.0001375688,0.00007073239,0.00005712535],"domain_scores_gemma":[0.999772,0.000026752,0.00007519044,0.00003520705,0.00002805541,0.00006285441],"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":[0.0003705251,0.0000292627,0.001689197,0.00003092809,0.0000255319,0.00005226245,0.00002590011,0.0001020626,0.9952949,0.0001184686,0.0001150866,0.002145906],"study_design_scores_gemma":[0.00004517115,0.0007119732,0.1198526,0.00004773586,0.0001361022,0.0009631453,0.0001391972,0.004057566,0.8664185,0.0007095776,0.006880349,0.00003800554],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840698,0.0008859567,0.009913864,0.0001819788,0.00002675819,0.00002547785,0.003708766,0.0002248538,0.0009624813],"genre_scores_gemma":[0.9677423,0.001790245,0.0154243,0.0007061211,0.00002285426,0.000128159,0.008863207,0.0003837601,0.004939073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001053821,"threshold_uncertainty_score":0.002620757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02895144022406835,"score_gpt":0.3162611541497463,"score_spread":0.2873097139256779,"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."}}