{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003198999,0.002130945,0.003191703,0.002628296,0.0003626051,0.001551334,0.004471756,0.001876545,0.002249005],"category_scores_gemma":[0.005426856,0.00111511,0.0008130299,0.004395507,0.001570927,0.002887451,0.001354157,0.003149958,0.003975864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001019695,"about_ca_system_score_gemma":0.001157168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002631555,"about_ca_topic_score_gemma":0.002777521,"domain_scores_codex":[0.9989127,0.0001820551,0.00009189416,0.0002330524,0.0005403181,0.00004000643],"domain_scores_gemma":[0.9963028,0.002219023,0.0002010116,0.0002499505,0.0009508305,0.00007634358],"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":[0.00006637835,0.00007636467,0.0003341626,0.003376301,0.0001446648,0.00004703628,0.00002336267,0.004981369,0.004480691,0.006606593,0.0218043,0.9580588],"study_design_scores_gemma":[0.0001133477,0.0002012486,0.002296056,0.003025818,0.0003909464,0.001555405,0.00007777056,0.05098207,0.02606245,0.06840061,0.8466467,0.0002476397],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0009125149,0.7952554,0.1964325,0.001821572,0.001570708,0.00006797639,0.0004354932,0.0008108068,0.002693046],"genre_scores_gemma":[0.006491048,0.8104168,0.1760739,0.001046667,0.001267341,0.0001451845,0.0010174,0.000151341,0.003390245],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004471756,"threshold_uncertainty_score":0.01691812,"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."}}