{"id":"W2162792752","doi":"10.1093/bioinformatics/btq249","title":"PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes","year":2010,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2574,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Simon Fraser University; Michael Smith Health Research BC; Cystic Fibrosis Foundation","keywords":"Proteome; Protein subcellular localization prediction; Archaea; Computer science; Subcellular localization; Precision and recall; Computational biology; Interface (matter); Proteomics; Software; Metagenomics; Biology; Bioinformatics; Data mining; Artificial intelligence; Bacteria; Cytoplasm; Genetics; Gene; Programming language","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":[0.00230192,0.002018392,0.001519675,0.001095361,0.000555147,0.001442636,0.00143963,0.0006951142,0.004834733],"category_scores_gemma":[0.005074054,0.000781537,0.00153168,0.001200974,0.0002976581,0.001218013,0.001456018,0.001624487,0.004395199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005243601,"about_ca_system_score_gemma":0.001009831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003062548,"about_ca_topic_score_gemma":0.004309738,"domain_scores_codex":[0.9991978,0.000168045,0.00007287906,0.0002385616,0.0002548622,0.00006780422],"domain_scores_gemma":[0.9984868,0.000557114,0.0001998914,0.0002085026,0.0004101473,0.000137578],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.007678094,0.0007391932,0.0608428,0.00285007,0.001412427,0.001081672,0.0004568542,0.1062439,0.3123614,0.00339053,0.169241,0.3337022],"study_design_scores_gemma":[0.0005461889,0.0004893991,0.01969438,0.0001385185,0.0004012526,0.0009964992,0.00007256969,0.8062905,0.1322566,0.003538151,0.0353558,0.0002200911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2500788,0.001873041,0.5257825,0.001338273,0.0002125625,0.0003313535,0.03199696,0.1845153,0.003871135],"genre_scores_gemma":[0.2771847,0.0006646733,0.6211372,0.0004525877,0.00008731647,0.0005964051,0.08416393,0.0111011,0.004612062],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004834733,"threshold_uncertainty_score":0.01617384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004537457218943714,"score_gpt":0.2113114192828754,"score_spread":0.2067739620639316,"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."}}