{"id":"W1998936881","doi":"10.1021/ac020105f","title":"High-Throughput Global Peptide Proteomic Analysis by Combining Stable Isotope Amino Acid Labeling and Data-Dependent Multiplexed-MS/MS","year":2002,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Canadian Institute for Theoretical Astrophysics","keywords":"Chemistry; Isobaric labeling; Peptide; Lysine; Tandem mass spectrometry; Tandem mass tag; Amino acid; Mass spectrometry; Phosphopeptide; Peptide sequence; Chromatography; Quantitative proteomics; Bottom-up proteomics; Biochemistry; Peptide mass fingerprinting; Glutamine; Proteomics; Protein mass spectrometry","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001780431,0.0003940411,0.0005812107,0.00002398717,0.0002591292,0.0001607311,0.0008053562,0.0003037729,0.00210767],"category_scores_gemma":[0.0001393908,0.0004125761,0.0001294029,0.0004825194,0.0001997346,0.000242906,0.0006668567,0.0005219228,0.00003326187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002156192,"about_ca_system_score_gemma":0.00002536548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000346701,"about_ca_topic_score_gemma":0.00001423071,"domain_scores_codex":[0.9972532,0.00001221358,0.0006094463,0.00115395,0.0003746499,0.0005964765],"domain_scores_gemma":[0.9977759,0.00006912454,0.0002310515,0.001519927,0.00009214291,0.0003119198],"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":[0.00003899064,0.0004578488,0.01631293,0.0002704666,0.0009018049,0.00002570294,0.00003444717,0.0002993526,0.9729683,0.0004766615,0.007016684,0.001196754],"study_design_scores_gemma":[0.001073596,0.00001708156,0.00005478782,0.00006249409,0.001027891,0.00002698857,0.00009724036,0.09661489,0.8936915,0.001158524,0.005328661,0.0008463748],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9583771,0.0007681157,0.02164284,0.0006879835,0.00001365208,0.0003253027,0.002065475,0.0004887604,0.01563079],"genre_scores_gemma":[0.9605968,0.0002728796,0.03359847,0.0001120883,0.0001261008,0.0001146597,0.001117082,0.0000415018,0.004020405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09631554,"threshold_uncertainty_score":0.9998326,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195757077971612,"score_gpt":0.286268754689537,"score_spread":0.2643111839098209,"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."}}