{"id":"W3008641372","doi":"10.1093/bioinformatics/btaa136","title":"PSORTm: a bacterial and archaeal protein subcellular localization prediction tool for metagenomics data","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Simon Fraser University; Genome British Columbia; Genome Canada","keywords":"Metagenomics; In silico; Computational biology; Identification (biology); Biology; Human Microbiome Project; Microbiome; Source code; Normalization (sociology); Biological classification; Computer science; Bioinformatics; Genetics; Ecology; Gene; Evolutionary biology","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":[0.003989723,0.002573125,0.001399734,0.00355056,0.0007988072,0.001554412,0.001808726,0.001321716,0.00319977],"category_scores_gemma":[0.008643083,0.0008427495,0.00234743,0.002270286,0.0004391883,0.001679179,0.001793276,0.001673876,0.00284536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000579006,"about_ca_system_score_gemma":0.00110822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001026517,"about_ca_topic_score_gemma":0.001390791,"domain_scores_codex":[0.998728,0.0003085,0.0001468484,0.0004078705,0.0003472178,0.00006168011],"domain_scores_gemma":[0.9977253,0.00114613,0.0004497977,0.000307765,0.0002110859,0.0001598623],"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":[0.006139184,0.0008807472,0.06436895,0.006745257,0.002688922,0.002651112,0.001423245,0.07189058,0.2416079,0.007301292,0.1196959,0.474607],"study_design_scores_gemma":[0.000435991,0.0007258407,0.02180234,0.0004475194,0.0004239777,0.001376792,0.0002579943,0.8058559,0.09870395,0.01185336,0.05786335,0.0002530282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06572302,0.001382048,0.6197974,0.000631027,0.0001478205,0.0004014891,0.04113083,0.2695969,0.001189436],"genre_scores_gemma":[0.1693969,0.0005522332,0.7694876,0.000273259,0.00007447977,0.0009229627,0.05197573,0.006576877,0.0007399541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003989723,"threshold_uncertainty_score":0.02109993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0358246009567333,"score_gpt":0.2247867421619844,"score_spread":0.1889621412052511,"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."}}