{"id":"W2109628189","doi":"10.1142/s0219720013500078","title":"<i>DE NOVO</i> SEQUENCING WITH LIMITED NUMBER OF POST-TRANSLATIONAL MODIFICATIONS PER PEPTIDE","year":2013,"lang":"en","type":"article","venue":"Journal of Bioinformatics and Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational biology; Posttranslational modification; Computer science; Proteomics; Peptide; Sequence (biology); DNA sequencing; Tandem mass spectrometry; Biology; Identification (biology); Chemistry; Genetics; Mass spectrometry; 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":[0.00008112586,0.00008189337,0.0001480986,0.00005537328,0.00006595979,0.00002067065,0.00009937409,0.00006626244,0.0001556564],"category_scores_gemma":[0.00001543243,0.00006253685,0.00004537732,0.00006534089,0.0001097006,0.0001677413,0.00001540586,0.000139875,0.000004699585],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002563092,"about_ca_system_score_gemma":0.0001442503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000010078,"about_ca_topic_score_gemma":5.247089e-7,"domain_scores_codex":[0.9992676,0.000006411384,0.000479859,0.00005220965,0.00009434982,0.00009960569],"domain_scores_gemma":[0.9987141,0.0001370463,0.0004466041,0.00006772902,0.0005745994,0.00005994809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003058424,0.0005087451,0.05421053,0.000674761,0.0006932686,0.000004147891,0.005268567,0.09466606,0.3697613,0.4277264,0.000722934,0.04545738],"study_design_scores_gemma":[0.0040179,0.0006957842,0.01443949,0.0004337337,0.0002212552,0.005453524,0.003965451,0.488034,0.02757416,0.4475031,0.006580418,0.001081206],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6371413,0.00003024252,0.3593969,0.0007214065,0.00000604819,0.00006688395,0.00005400249,0.000009990978,0.002573129],"genre_scores_gemma":[0.6003989,0.00002314194,0.3993539,0.0001297274,0.00002086239,0.000007516803,0.00004051808,0.000004187958,0.00002126386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3933679,"threshold_uncertainty_score":0.2550178,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181630914459508,"score_gpt":0.2614766387186723,"score_spread":0.2496603295740772,"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."}}