{"id":"W1964328150","doi":"10.1038/nmeth.2702","title":"Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition","year":2013,"lang":"en","type":"article","venue":"Nature Methods","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":324,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Spinal Cord Injury BC; Sciex (Canada); Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Human Genome Research Institute","keywords":"Computational biology; Protein–protein interaction; Mass spectrometry; Proteomics; Pipeline (software); Systems biology; Biology; Chemistry; Computer science; Cell biology; Biochemistry; Gene; Chromatography","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":[0.0008160026,0.0009744286,0.0008130733,0.001331257,0.001039047,0.001436614,0.001106132,0.0007768979,0.003047471],"category_scores_gemma":[0.001997157,0.000769286,0.0007597726,0.0009830849,0.0005616816,0.001120724,0.001636873,0.00182269,0.001941069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007569019,"about_ca_system_score_gemma":0.0008999303,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876094,"about_ca_topic_score_gemma":0.005435162,"domain_scores_codex":[0.9991156,0.00007981322,0.00005256169,0.0002451385,0.0003851674,0.0001216679],"domain_scores_gemma":[0.9989591,0.0003316242,0.00013616,0.0002265552,0.0002530425,0.00009356255],"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":[0.0001679199,0.00004595717,0.001081102,0.0001978363,0.00006035359,0.00006355707,0.00004009752,0.0004311064,0.9808987,0.0008933648,0.0008492739,0.01527091],"study_design_scores_gemma":[0.00007216484,0.00009024071,0.01393439,0.00002796746,0.0001025227,0.0009773297,0.00008546541,0.04831615,0.9221218,0.003951001,0.0102479,0.00007306194],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3957185,0.002311124,0.5853437,0.001185043,0.0002065219,0.0004001482,0.003834196,0.004935127,0.006065656],"genre_scores_gemma":[0.581109,0.001930425,0.4050041,0.0007491115,0.00006937362,0.000527931,0.004914616,0.0009135377,0.004781946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003047471,"threshold_uncertainty_score":0.01019478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02143057429859174,"score_gpt":0.3418835128823691,"score_spread":0.3204529385837774,"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."}}