{"id":"W4211185070","doi":"10.1038/s43586-021-00029-y","title":"Subcellular proteomics","year":2021,"lang":"en","type":"article","venue":"Nature Reviews Methods Primers","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of British Columbia; Canada's Michael Smith Genome Sciences Centre; Sinai Health System; Lunenfeld-Tanenbaum Research Institute","funders":"National Center for Advancing Translational Sciences; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of General Medical Sciences; National Institute on Aging","keywords":"Proteomics; Proteome; Organelle; Biology; Cell biology; Protein subcellular localization prediction; Cell fractionation; Computational biology; Quantitative proteomics; Cell; Bioinformatics; Biochemistry; Enzyme; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008280392,0.0002226189,0.0004472378,0.00002644482,0.0001076107,0.00002748502,0.0003054659,0.000461479,0.0006406949],"category_scores_gemma":[0.0006304909,0.0002013588,0.0002673985,0.0003398336,0.0000475766,0.00006697102,0.0001200007,0.001162279,0.00003719777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008657976,"about_ca_system_score_gemma":0.00009542804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001471793,"about_ca_topic_score_gemma":3.895128e-7,"domain_scores_codex":[0.9985473,0.0001635414,0.0004215814,0.0004852749,0.0001228936,0.0002594405],"domain_scores_gemma":[0.9985203,0.0001092131,0.0002340852,0.0009116309,0.0001161324,0.0001086178],"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":[0.000003053021,0.0000290828,0.00002113914,0.0001717227,0.00001425957,0.000004358495,0.0000119695,6.972047e-7,0.772153,0.007215089,0.0005852417,0.2197904],"study_design_scores_gemma":[0.00005187075,0.000001704519,9.234313e-7,0.00005203266,0.0000235017,0.00001492671,0.000005457226,0.00001700773,0.5049492,0.004584048,0.4901749,0.0001244585],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000605509,0.1373262,0.8268476,0.000602359,0.0001026161,0.0005864017,0.00001298125,0.0002367472,0.03367962],"genre_scores_gemma":[0.0001322837,0.02110261,0.9750183,0.0004759145,0.0001417759,0.000361925,0.00005668994,0.00004102192,0.002669534],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4895896,"threshold_uncertainty_score":0.8211175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02963589867468509,"score_gpt":0.4128733516588744,"score_spread":0.3832374529841893,"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."}}