{"id":"W2151790371","doi":"10.1093/bioinformatics/btg447","title":"Predicting subcellular localization of proteins using machine-learned classifiers","year":2004,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":334,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Subcellular localization; Computer science; Artificial intelligence; Protein subcellular localization prediction; Machine learning; Computational biology; Pattern recognition (psychology); Chemistry; Biology; Biochemistry; Cytoplasm; Gene","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":[0.001605731,0.0009999998,0.0007692644,0.001827884,0.0005086212,0.001224818,0.0008635198,0.001301174,0.001374691],"category_scores_gemma":[0.005892015,0.0002012723,0.0005536355,0.001272891,0.0003884324,0.001383709,0.0003916012,0.001137074,0.001704004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000727628,"about_ca_system_score_gemma":0.0009743246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003008402,"about_ca_topic_score_gemma":0.002507197,"domain_scores_codex":[0.9992713,0.0001848781,0.00007172086,0.0001886365,0.000188052,0.00009529493],"domain_scores_gemma":[0.9945561,0.003391995,0.0004762525,0.0002372805,0.001192959,0.0001454284],"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":[0.0006753776,0.0007225715,0.1302864,0.0007647161,0.0002735602,0.0006887997,0.0001330424,0.3642166,0.02794119,0.002958906,0.0189751,0.4523638],"study_design_scores_gemma":[0.00003002292,0.00008338333,0.006273776,0.00005533452,0.00007263832,0.0001989223,0.00004289263,0.9678016,0.01913107,0.004038227,0.002253691,0.00001845828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5351475,0.003599474,0.44152,0.001714312,0.0002897501,0.0001707505,0.005740761,0.006227084,0.005590328],"genre_scores_gemma":[0.8100172,0.0009264064,0.1773298,0.0003020031,0.0001677151,0.0001253299,0.00926194,0.000101916,0.00176776],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003008402,"threshold_uncertainty_score":0.008492053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01636032850905748,"score_gpt":0.2565539395148381,"score_spread":0.2401936110057807,"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."}}