{"id":"W1984673676","doi":"10.1142/s0219720008003291","title":"COMPLEXITIES AND ALGORITHMS FOR GLYCAN SEQUENCING USING TANDEM MASS SPECTROMETRY","year":2008,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Glycosylation and Glycoproteins Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Center for Advanced Study, University of Illinois at Urbana-Champaign; Natural Sciences and Engineering Research Council of Canada; Tsinghua University","keywords":"Glycan; Tandem mass spectrometry; Computational biology; Glycopeptide; Computer science; Mass spectrometry; Proteomics; Algorithm; Biology; Chemistry; Biochemistry; Gene; Chromatography","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":[0.003593358,0.001931911,0.001626901,0.002431248,0.001816551,0.003069936,0.003795234,0.002051399,0.00497456],"category_scores_gemma":[0.02081894,0.00150855,0.002203851,0.003749948,0.001516291,0.004641412,0.003075772,0.003400039,0.002337155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001771562,"about_ca_system_score_gemma":0.002917224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004549816,"about_ca_topic_score_gemma":0.003844276,"domain_scores_codex":[0.9964957,0.001250984,0.0002719936,0.0005482811,0.001226407,0.0002066979],"domain_scores_gemma":[0.9847385,0.01141989,0.000797341,0.001422137,0.001383359,0.0002388155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003632093,0.000204627,0.00156558,0.000656078,0.0001374614,0.0004594876,0.0002542348,0.5632086,0.004474307,0.08266254,0.01104256,0.3349713],"study_design_scores_gemma":[0.00006825514,0.00002922851,0.0002478687,0.00003033808,0.00002097815,0.0001688127,0.00004694979,0.8574368,0.001504864,0.136603,0.003814864,0.00002806365],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003353582,0.0003632891,0.9933783,0.0004347695,0.00004283073,0.0001048375,0.0001372595,0.0008538424,0.00133131],"genre_scores_gemma":[0.0331661,0.0005382405,0.9637533,0.0001365335,0.00009499541,0.0003864318,0.0005860336,0.00024608,0.001092242],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00497456,"threshold_uncertainty_score":0.01900369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05674675018465132,"score_gpt":0.3167456891431366,"score_spread":0.2599989389584852,"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."}}