{"id":"W3176329976","doi":"10.1038/s41592-021-01194-4","title":"Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments","year":2021,"lang":"en","type":"review","venue":"Nature Methods","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Michael Smith Health Research BC; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Compute Canada","keywords":"Interactome; Computational biology; Computer science; Proteome; Mass spectrometry; Fractionation; Resource (disambiguation); Inference; Benchmark (surveying); Betweenness centrality; Proteomics; Biological system; Data mining; Chemistry; Biology; Bioinformatics; Chromatography; Artificial intelligence; Mathematics; Biochemistry; Centrality; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00245498,0.000300808,0.00216897,0.0003857923,0.00009153692,0.00004843744,0.0002630843,0.0007832305,0.00005154926],"category_scores_gemma":[0.0002401004,0.0001804705,0.00298524,0.001202148,0.00005099534,0.000003193165,0.00007963399,0.0002964826,1.527632e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001944981,"about_ca_system_score_gemma":0.00009125092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002024641,"about_ca_topic_score_gemma":0.000002432445,"domain_scores_codex":[0.998063,0.0006019581,0.0006277731,0.000387729,0.0001619327,0.0001576611],"domain_scores_gemma":[0.9975374,0.0009212324,0.0007198043,0.0006091071,0.0001722684,0.0000402303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001366136,0.00003011917,0.000002926003,0.0007008011,0.8009496,3.04528e-7,0.0000210773,0.0010246,0.0003623726,0.0007749848,0.000353531,0.195766],"study_design_scores_gemma":[0.00004482321,0.00002271934,0.000004051898,0.00001047053,0.4495921,0.000001794808,0.00001285617,0.001746412,0.0008646311,0.00003449032,0.5475204,0.0001451802],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[2.92514e-7,0.5245182,0.4749948,0.00001192382,0.00005296811,0.0002772967,0.00009260987,0.000001696099,0.00005015189],"genre_scores_gemma":[0.000005055226,0.5769932,0.4215573,0.0000448297,0.00005821186,0.0001097556,0.0009558551,0.00001320139,0.0002626314],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.5471669,"threshold_uncertainty_score":0.7359374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1770132175653771,"score_gpt":0.4639283162625721,"score_spread":0.286915098697195,"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."}}