{"id":"W4412188000","doi":"10.1002/pmic.70010","title":"Optimizing Proximity Proteomics on the EvoSep‐timsTOF LC–MS System","year":2025,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Government of Ontario; Ontario Genomics; Genome Canada","keywords":"Biotinylation; Streptavidin; Biotin; Proteomics; Chromatography; Mass spectrometry; Chemistry; Throughput; Computational biology; Computer science; Biology; Biochemistry","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":[0.001384743,0.0007867417,0.0008180478,0.0004889237,0.0005570241,0.0008728357,0.0008661312,0.0006298723,0.001236503],"category_scores_gemma":[0.0009871894,0.0003578303,0.0004207893,0.0004254861,0.0003066518,0.0007754183,0.0007752591,0.0008758394,0.001503675],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007113015,"about_ca_system_score_gemma":0.0008909242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008477831,"about_ca_topic_score_gemma":0.002583273,"domain_scores_codex":[0.9987071,0.0001874534,0.00009959163,0.00032196,0.0005502352,0.0001336442],"domain_scores_gemma":[0.9997334,0.00005780365,0.00003942881,0.00002302373,0.0001090358,0.00003728988],"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":[0.00009007763,0.00004258343,0.0003284653,0.0000765877,0.00001689106,0.00003705394,0.00001982857,0.000320198,0.9951763,0.0001325168,0.000222177,0.003537356],"study_design_scores_gemma":[0.00002210137,0.0001557919,0.002170383,0.0000134811,0.00002878628,0.0001863172,0.00002361558,0.008405495,0.9842585,0.0001320476,0.004574458,0.00002903527],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7588519,0.001960921,0.2304105,0.0004579099,0.0001305389,0.0007266489,0.001818905,0.002642007,0.003000611],"genre_scores_gemma":[0.4805053,0.002038595,0.5052269,0.0004654569,0.00006579779,0.001519497,0.005319557,0.0008144877,0.004044436],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001384743,"threshold_uncertainty_score":0.007323325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00850211522708758,"score_gpt":0.2277108526554297,"score_spread":0.2192087374283421,"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."}}