{"id":"W2074156599","doi":"10.1142/9789812776136_0058","title":"EPILOC: A (WORKING) TEXT-BASED SYSTEM FOR PREDICTING PROTEIN SUBCELLULAR LOCATION","year":2007,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence","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":[0.001428055,0.001418799,0.000837522,0.003900977,0.0005439436,0.001026588,0.001905611,0.001633014,0.01390088],"category_scores_gemma":[0.006683664,0.0003582867,0.000489218,0.001791805,0.0003042168,0.003556576,0.001435335,0.0006496322,0.01102758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000503481,"about_ca_system_score_gemma":0.0005725559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001337845,"about_ca_topic_score_gemma":0.001597109,"domain_scores_codex":[0.9994043,0.0001093395,0.00006550465,0.0002463135,0.000138334,0.00003616365],"domain_scores_gemma":[0.9961621,0.00182625,0.0003881508,0.0005700476,0.0007864819,0.0002669587],"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":[0.002901646,0.0004145176,0.007581891,0.001186388,0.0001521001,0.0009072851,0.0003471224,0.007344781,0.05890078,0.003792528,0.2248924,0.6915786],"study_design_scores_gemma":[0.000372151,0.001003759,0.01329862,0.0002049236,0.0002105259,0.001922201,0.0003647408,0.68564,0.1415402,0.01559488,0.1396109,0.0002370236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06635587,0.001109898,0.4814332,0.001088301,0.0004614707,0.0005236588,0.05215211,0.3885042,0.008371226],"genre_scores_gemma":[0.2257006,0.0006476839,0.6535019,0.001042064,0.0003850118,0.0007415008,0.09751274,0.004517416,0.01595101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01390088,"threshold_uncertainty_score":0.04650307,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00840131759235885,"score_gpt":0.2408423958874599,"score_spread":0.2324410782951011,"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."}}