{"id":"W2015407064","doi":"10.1083/jcb.200805092","title":"Identifying specific protein interaction partners using quantitative mass spectrometry and bead proteomes","year":2008,"lang":"en","type":"article","venue":"The Journal of Cell Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":446,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Biotechnology and Biological Sciences Research Council; Directorate for Biological Sciences; Medical Research Council; Engineering and Physical Sciences Research Council; Wellcome Trust; University of Dundee","keywords":"Tandem affinity purification; Stable isotope labeling by amino acids in cell culture; Proteome; Biology; Protein–protein interaction; Quantitative proteomics; Immunoprecipitation; Mass spectrometry; Affinity chromatography; Plasma protein binding; Fusion protein; Computational biology; Biochemistry; Proteomics; Chemistry; Chromatography; Recombinant DNA; Enzyme","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":[0.002855158,0.001073839,0.001086256,0.001529807,0.0005688288,0.001776222,0.001307487,0.001102454,0.001110013],"category_scores_gemma":[0.002722568,0.0005771454,0.0005561949,0.0013244,0.0008454698,0.001208923,0.001050854,0.001618826,0.001237428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007481784,"about_ca_system_score_gemma":0.0005381356,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005342282,"about_ca_topic_score_gemma":0.00100958,"domain_scores_codex":[0.9964324,0.0007758968,0.0002490037,0.000756375,0.001604738,0.0001815698],"domain_scores_gemma":[0.9980903,0.0009354383,0.0003115323,0.0002885581,0.0002759097,0.00009823303],"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":[0.00009952807,0.00004697738,0.0007591062,0.0002055421,0.00003214465,0.00002647848,0.00002527561,0.0004582714,0.9878002,0.001061432,0.0002178423,0.009267252],"study_design_scores_gemma":[0.0000159182,0.00009739797,0.00249864,0.0000138731,0.00002999847,0.0002239531,0.00002716027,0.01193511,0.979513,0.001238524,0.004372866,0.00003353008],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1493418,0.004242264,0.8404043,0.0004469198,0.0001180439,0.0002740013,0.001649177,0.001834861,0.001688722],"genre_scores_gemma":[0.3234971,0.004641247,0.6633088,0.0005585339,0.000108017,0.0009908048,0.003234605,0.0003685639,0.003292247],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002855158,"threshold_uncertainty_score":0.01509964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07324135592246396,"score_gpt":0.3588865964644963,"score_spread":0.2856452405420323,"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."}}