{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002062579,0.0001386553,0.0002612579,0.0001598064,0.0001659234,0.00002246112,0.0001738867,0.0001201982,0.00002941856],"category_scores_gemma":[0.00003612933,0.0001584913,0.00004168009,0.0003045146,0.0001673086,0.0001296788,0.000196735,0.000172881,3.259951e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005515347,"about_ca_system_score_gemma":0.00007697842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004684906,"about_ca_topic_score_gemma":0.00002376243,"domain_scores_codex":[0.9988708,0.000008015173,0.0003973863,0.0004661866,0.000110032,0.0001475875],"domain_scores_gemma":[0.9991235,0.00005678446,0.0002338764,0.0004631196,0.00007039556,0.00005238075],"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":[0.0001995812,0.0001982491,0.05398031,0.0001511643,0.0004784038,0.000004574444,0.000381714,0.0079674,0.9177808,0.01193343,0.00008259325,0.006841751],"study_design_scores_gemma":[0.001208982,0.000038273,0.03252492,0.00006725529,0.0006701396,0.000186349,0.0001468652,0.8446691,0.1001548,0.009599785,0.009858519,0.0008749975],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8108295,0.000307562,0.1881048,0.0001358896,0.00001155436,0.0001687632,0.0002850012,0.00002133379,0.0001355647],"genre_scores_gemma":[0.6783492,0.001918821,0.318736,0.0001096305,0.0000700512,0.00005089942,0.0006405956,0.00002992387,0.0000948833],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8367018,"threshold_uncertainty_score":0.6463087,"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."}}