{"id":"W2111105704","doi":"10.5376/cmb.2014.04.0007","title":"FunSecKB2: a fungal protein subcellular location knowledgebase","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computational biology; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00124529,0.002260225,0.002294116,0.006226675,0.001420769,0.003171529,0.003247013,0.002077149,0.01987773],"category_scores_gemma":[0.003460069,0.001120708,0.001515755,0.006930944,0.0003702441,0.003593326,0.002623202,0.002022186,0.0208895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001083543,"about_ca_system_score_gemma":0.003067087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006658998,"about_ca_topic_score_gemma":0.005021374,"domain_scores_codex":[0.9992267,0.0000870911,0.0001419345,0.000197241,0.0002542576,0.00009272939],"domain_scores_gemma":[0.9984567,0.0003752024,0.0002465973,0.0003148716,0.0003948449,0.0002118125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00190538,0.0003768606,0.004336869,0.007958476,0.0003757671,0.002188819,0.0005383954,0.007529665,0.04094715,0.008225251,0.6034027,0.3222147],"study_design_scores_gemma":[0.0003889473,0.0001278398,0.008584251,0.001318643,0.0003101893,0.002329943,0.0002372936,0.02013589,0.01916027,0.01043012,0.9367152,0.0002614146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01678202,0.009409185,0.1111265,0.0008974809,0.0005084741,0.0004490245,0.7695349,0.07651104,0.01478143],"genre_scores_gemma":[0.01230022,0.003186539,0.05730801,0.0002897692,0.00004517256,0.0002976542,0.9216452,0.001900508,0.003026856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01987773,"threshold_uncertainty_score":0.06649762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00442848962742088,"score_gpt":0.2555454032499011,"score_spread":0.2511169136224802,"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."}}