{"id":"W2161594258","doi":"10.1109/icdmw.2008.130","title":"Plant Protein Localization Using Discriminative and Frequent Partition-Based Subsequences","year":2008,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Extracellular; Discriminative model; Partition (number theory); Computer science; Classifier (UML); Protein subcellular localization prediction; Artificial intelligence; Computational biology; Biology; Biochemistry; Mathematics; Gene; Combinatorics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007053879,0.0000831444,0.00006234748,0.00002436223,0.0001278294,0.00001277453,0.00004699898,0.00005519515,0.00002360508],"category_scores_gemma":[0.00006030149,0.00006775607,0.00001804946,0.00004044182,0.0001240263,0.000004776309,0.00002271911,0.00004084004,0.000002031212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009541926,"about_ca_system_score_gemma":0.00005247619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007880887,"about_ca_topic_score_gemma":0.00003292554,"domain_scores_codex":[0.9995175,0.00003663001,0.0001298556,0.0001196788,0.0000899226,0.0001064191],"domain_scores_gemma":[0.9997523,0.000005451056,0.00006140632,0.00009845432,0.00004196579,0.0000404301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002081293,0.0002828342,0.1991594,0.0004132954,0.0001188224,0.00003875609,0.001689426,0.04301582,0.7438982,0.007706854,0.001914077,0.00155438],"study_design_scores_gemma":[0.0006229768,0.0003955746,0.005036181,0.00006103566,0.0000176898,0.00007638738,0.0002299564,0.3280963,0.6616977,0.0002333242,0.003151502,0.0003813228],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8768435,0.00005447809,0.121853,0.00007386957,0.00002088697,0.0001697424,0.00001148572,0.00001462839,0.0009584793],"genre_scores_gemma":[0.9896097,0.00001265772,0.009817188,0.0002109626,0.00003089619,0.00001099318,0.0001753015,0.000006745055,0.0001255847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2850805,"threshold_uncertainty_score":0.2763012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02463159534744171,"score_gpt":0.2558072729686056,"score_spread":0.2311756776211638,"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."}}