{"id":"W4229449348","doi":"10.1038/s41592-022-01454-x","title":"Understudied proteins: opportunities and challenges for functional proteomics","year":2022,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":221,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sinai Health System; Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"Biotechnology and Biological Sciences Research Council; University of California, San Diego; Science for Life Laboratory; Department of Biochemistry, University of Cambridge; University of Toronto; Kungliga Tekniska Högskolan; Deutsche Forschungsgemeinschaft; Westlake University; Wellcome; European Molecular Biology Laboratory; European Bioinformatics Institute; Medical Research Council; Technische Universität Berlin; Francis Crick Institute; Wellcome Trust","keywords":"Computational biology; Proteomics; Function (biology); Protein function; Biology; Data science; Posttranslational modification; Bioinformatics; Computer science; Genetics; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005087184,0.0001238527,0.0001493505,0.00003990374,0.0004542044,0.00001274459,0.0001256265,0.0001442731,0.00009857595],"category_scores_gemma":[0.000104158,0.0001250447,0.00005644751,0.0000510568,0.00005522099,0.00004149012,0.0001692354,0.0005542552,1.014723e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008010409,"about_ca_system_score_gemma":0.00004748197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001153409,"about_ca_topic_score_gemma":8.089851e-7,"domain_scores_codex":[0.9992613,0.00004415143,0.0001388374,0.0002882763,0.0001140845,0.0001533765],"domain_scores_gemma":[0.9993843,0.0001861677,0.0000983809,0.0002225967,0.00006595343,0.00004264478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002353453,0.0001458106,0.00001603257,0.0005275618,0.0001116464,0.000001980758,0.0003056239,0.00004554362,0.1807547,0.6096321,0.001097492,0.2071261],"study_design_scores_gemma":[0.0004217288,0.00006306059,0.00001412872,0.000009805585,0.00002663094,0.00002421638,0.001663641,0.0003801931,0.12094,0.156904,0.7193155,0.0002371256],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002223929,0.01710859,0.9532703,0.01418758,0.000130955,0.001589854,0.0002083795,0.0003696772,0.01091074],"genre_scores_gemma":[0.006901799,0.001552212,0.9820642,0.0002848514,0.000145933,0.007470219,0.00005475395,0.00003316957,0.001492855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.718218,"threshold_uncertainty_score":0.5099174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1545651353316641,"score_gpt":0.3857849533647095,"score_spread":0.2312198180330454,"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."}}