{"id":"W2097411884","doi":"10.5376/cmb.2012.02.0001","title":"Computational Prediction of Protein Subcellular Locations in Eukaryotes: an Experience Report","year":2012,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Signal peptide; Endoplasmic reticulum; Proteome; Transmembrane protein; Computational biology; Subcellular localization; Secretory protein; Protein targeting; Secretory pathway; Protein Sorting Signals; Computational model; Computer science; Membrane protein; Biology; Cell biology; Bioinformatics; Biochemistry; Secretion; Peptide sequence; Golgi apparatus; Artificial intelligence; Membrane; Gene; Cytoplasm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390386,0.0001465488,0.0001540925,0.0001300429,0.00005303361,0.000007896431,0.0001816819,0.0001619737,0.00002017175],"category_scores_gemma":[0.0002219044,0.0001571723,0.00005684623,0.0001696425,0.0001789122,0.00001361021,0.0000858198,0.000115705,0.000006645433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002390404,"about_ca_system_score_gemma":0.0001168001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002205007,"about_ca_topic_score_gemma":0.000002353576,"domain_scores_codex":[0.9986629,0.0001705924,0.000518344,0.0002475317,0.0001618706,0.0002387333],"domain_scores_gemma":[0.9992211,0.00002498121,0.0002253072,0.0002559769,0.0001830348,0.00008960976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00006710752,0.0005115247,0.3102756,0.00005872954,0.00006459837,0.00001048765,0.0007359671,0.4190045,0.2318205,0.03631828,0.00004575614,0.001086907],"study_design_scores_gemma":[0.003281172,0.00197354,0.4681677,0.0001214493,0.00005391611,0.001082099,0.0006265351,0.3976826,0.05452541,0.06665672,0.004511581,0.001317197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6064494,0.0001698618,0.392803,0.00004444463,0.0000706038,0.0001883527,0.00002526686,0.00001216334,0.0002368689],"genre_scores_gemma":[0.9438656,0.000001112334,0.05258024,0.0001146454,0.00007480443,0.00005307151,0.003269686,0.00001564571,0.00002516608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3402228,"threshold_uncertainty_score":0.6409299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228365564142645,"score_gpt":0.2999240799302989,"score_spread":0.2876404242888724,"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."}}