{"id":"W2134472681","doi":"10.1093/bfgp/eln017","title":"Analysis of iTRAQ data using Mascot and Peaks quantification algorithms","year":2008,"lang":"en","type":"article","venue":"Briefings in Functional Genomics and Proteomics","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bioinformatics Solutions (Canada)","funders":"National Science Foundation","keywords":"Mascot; Mass spectrometry; Software; Proteomics; Quantitative proteomics; Field (mathematics); Computer science; Peptide; Biology; Computational biology; Algorithm; Biological system; Chromatography; Chemistry; Biochemistry; Mathematics","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":[0.01068659,0.004021069,0.003354214,0.009463691,0.001711002,0.003073459,0.003744476,0.001356813,0.01222363],"category_scores_gemma":[0.01209399,0.0008845605,0.002214025,0.008904921,0.001013003,0.003019036,0.00209217,0.002382514,0.0106476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006456203,"about_ca_system_score_gemma":0.001443212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006744042,"about_ca_topic_score_gemma":0.0007385109,"domain_scores_codex":[0.9924624,0.001450644,0.001426865,0.001396842,0.002931319,0.0003319461],"domain_scores_gemma":[0.9941574,0.001822406,0.001026371,0.0007976973,0.002004179,0.0001919179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.006537799,0.0007037167,0.01143944,0.008453749,0.002124225,0.003001,0.001538805,0.007009266,0.6136566,0.01168652,0.066016,0.2678329],"study_design_scores_gemma":[0.0007318727,0.002065424,0.02860921,0.0006329254,0.0007587004,0.009161188,0.0008805653,0.1263409,0.5943463,0.02487706,0.2105199,0.001075832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08213633,0.004356902,0.8281679,0.0009220488,0.001347124,0.002269887,0.03245676,0.03682617,0.01151687],"genre_scores_gemma":[0.04594801,0.001827938,0.9087198,0.0007522505,0.0002109754,0.00254817,0.03125779,0.005929717,0.002805344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01222363,"threshold_uncertainty_score":0.05651677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08991167801736605,"score_gpt":0.2938022252463098,"score_spread":0.2038905472289437,"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."}}