{"id":"W3155660502","doi":"10.1038/s41467-021-22599-x","title":"Proteomics of protein trafficking by in vivo tissue-specific labeling","year":2021,"lang":"en","type":"article","venue":"Nature Communications","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Cancer Institute; U.S. Department of Health and Human Services; Government of Canada; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Howard Hughes Medical Institute","keywords":"Biotinylation; Proteomics; Cell biology; Biology; Protein subcellular localization prediction; Biotin; Secretory protein; Compartment (ship); DNA ligase; Cell; Organelle; Biochemistry; Secretion; Computational biology; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001018008,0.00007341433,0.0001012954,0.00002355309,0.00008580634,0.00000800244,0.0003141619,0.0003381657,0.000005955844],"category_scores_gemma":[0.00007859511,0.00007208926,0.00003329315,0.0001815513,0.00008599756,0.000001715706,0.0002280878,0.0004444356,0.000001031669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009955971,"about_ca_system_score_gemma":0.00004172491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004266517,"about_ca_topic_score_gemma":0.0001718738,"domain_scores_codex":[0.9994464,0.00007845216,0.0001724538,0.000143229,0.00005861316,0.0001008721],"domain_scores_gemma":[0.9991013,0.00001441112,0.00006062917,0.0006872468,0.0001184875,0.00001786218],"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":[0.000005812867,0.0001098661,0.0002162104,0.00001021056,0.0000231776,5.07415e-7,0.0000922328,0.000008156537,0.9946667,0.0007033867,0.003305618,0.0008581525],"study_design_scores_gemma":[0.0001933829,0.00001899439,0.00008775132,0.00004336681,0.000004078861,0.000002409058,0.0001445976,0.00001499499,0.7497864,0.00002003911,0.2496102,0.00007378665],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6287715,0.3552975,0.00031654,0.006561964,0.0001857153,0.0006984922,0.00007263037,0.00002856143,0.008067044],"genre_scores_gemma":[0.9756216,0.007518936,0.01600714,0.00006921712,0.00002042031,0.00002577494,0.0001004847,0.00001099622,0.000625426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3477786,"threshold_uncertainty_score":0.2939715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01196256457326423,"score_gpt":0.2767482068675154,"score_spread":0.2647856422942512,"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."}}