{"id":"W2167844024","doi":"10.1038/nmeth.2557","title":"The CRAPome: a contaminant repository for affinity purification–mass spectrometry data","year":2013,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":1804,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Institute for Cancer Research; University of Toronto; Université de Montréal; Montreal Clinical Research Institute; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute on Drug Abuse; National Cancer Institute; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research","keywords":"Computational biology; Mass spectrometry; Tandem affinity purification; Chemistry; Protein purification; Proteome; Chromatography; Affinity chromatography; Biology; Biochemistry; Enzyme","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005114683,0.002027912,0.001661452,0.005771366,0.001639477,0.003835736,0.004611535,0.002107399,0.01471678],"category_scores_gemma":[0.01377816,0.001450184,0.001334414,0.006776,0.000956067,0.004043378,0.004721211,0.002988786,0.01791412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001176736,"about_ca_system_score_gemma":0.004944794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005353024,"about_ca_topic_score_gemma":0.008866828,"domain_scores_codex":[0.9969268,0.0002788587,0.0002776855,0.0006372951,0.001640614,0.0002386392],"domain_scores_gemma":[0.9886677,0.001802195,0.001057636,0.00540165,0.002053448,0.001017411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.005927142,0.0006326181,0.01808218,0.003540517,0.0009612612,0.001860341,0.00102381,0.006970961,0.1927728,0.008335331,0.6217377,0.1381554],"study_design_scores_gemma":[0.000832847,0.0003754387,0.022818,0.0004843253,0.0004674465,0.002003159,0.0003461914,0.03478616,0.1620201,0.009428217,0.7658865,0.0005515813],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.04568028,0.00159146,0.1465365,0.001308522,0.0003824575,0.0008119149,0.5165443,0.2759846,0.01115994],"genre_scores_gemma":[0.07546587,0.001209106,0.1647265,0.0006383237,0.0001019562,0.0007813525,0.722118,0.02874793,0.006210997],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01471678,"threshold_uncertainty_score":0.04923254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02834747888140295,"score_gpt":0.390944764862205,"score_spread":0.3625972859808021,"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."}}