{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002553419,0.00008575512,0.0001691452,0.0001321653,0.0001587244,0.00002589957,0.00006524268,0.00007709297,0.00000517004],"category_scores_gemma":[0.00007001418,0.00007091786,0.00005145043,0.00005873499,0.0001718486,0.00001551336,0.0000373588,0.00007600983,3.036454e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002043481,"about_ca_system_score_gemma":0.0001636982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000410622,"about_ca_topic_score_gemma":9.241573e-7,"domain_scores_codex":[0.9993125,0.00002550183,0.0003482605,0.00007296166,0.0001016861,0.0001390874],"domain_scores_gemma":[0.9993522,0.00005582882,0.0002168092,0.00004368696,0.0002542903,0.0000772101],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001658427,0.0002467985,0.04346506,0.0007789602,0.00170089,0.00006187313,0.003070306,0.02553691,0.8285954,0.038588,0.002958626,0.05333875],"study_design_scores_gemma":[0.007299418,0.005935107,0.01354953,0.00009876662,0.00006861849,0.008109706,0.002768441,0.8803688,0.02282641,0.04480541,0.01335744,0.0008123317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.646814,0.000465724,0.352312,0.0001196725,0.00005541856,0.00009178754,0.00002082159,0.000001835091,0.0001187043],"genre_scores_gemma":[0.7639095,0.0001752138,0.2355654,0.0001480386,0.000137457,0.000001179785,0.00003769907,0.00000498793,0.00002058201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8548319,"threshold_uncertainty_score":0.2891946,"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."}}