{"id":"W2036790151","doi":"10.1038/nrmicro1494","title":"Methods for predicting bacterial protein subcellular localization","year":2006,"lang":"en","type":"review","venue":"Nature Reviews Microbiology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":170,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"","keywords":"Subcellular localization; Protein subcellular localization prediction; Biology; Computational biology; Identification (biology); Bacterial protein; Drug target; Annotation; Genome; Bioinformatics; Genetics; Gene; Biochemistry","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001905651,0.0007319105,0.002166876,0.0001342237,0.0001385463,0.00004153265,0.0006203184,0.003343147,0.00002931686],"category_scores_gemma":[0.00108499,0.0005566538,0.0009923067,0.0002090876,0.0001007018,0.000004298198,0.0002381985,0.00103035,0.0000312337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006962821,"about_ca_system_score_gemma":0.0002118327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000282937,"about_ca_topic_score_gemma":0.000006451351,"domain_scores_codex":[0.9957995,0.001433698,0.001481137,0.0007398013,0.00004040858,0.0005054678],"domain_scores_gemma":[0.9975367,0.00009159352,0.001407387,0.0007613675,0.0001376727,0.00006524034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000016846,0.00003262312,0.000001931098,0.03164778,0.0001476505,5.278204e-7,0.000005247496,0.000002615274,0.01729823,0.00006496002,0.01442009,0.9363615],"study_design_scores_gemma":[0.0002318342,0.0001820469,3.29505e-8,0.003318516,0.0004551128,0.0001238908,4.89116e-7,0.0000335248,0.001041461,0.00001049105,0.9940786,0.0005239659],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000001808198,0.9009341,0.09345689,0.000009233619,0.0008303589,0.00431787,0.000197597,0.00003333329,0.0002188251],"genre_scores_gemma":[2.782514e-7,0.8758674,0.09759121,0.0001150546,0.00137701,0.0005835399,0.0234882,0.0001056516,0.0008717006],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9796585,"threshold_uncertainty_score":0.9996885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01785802078933187,"score_gpt":0.3771686864157439,"score_spread":0.359310665626412,"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."}}