{"id":"W1839112781","doi":"10.5376/cmb.2014.04.0004","title":"In Silico Proteomic Functional Re-annotation of <i>Escherichia coli</i> K-12 using Dynamic Biological Data Fusion Strategy","year":2014,"lang":"en","type":"article","venue":"Computational Molecular Biology","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Annotation; Proteome; Computational biology; In silico; Data integration; Genome project; Genome; Computer science; Biological database; Human proteome project; Biology; Proteomics; Bioinformatics; Gene; Genetics; Data mining","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":[0.0003826234,0.0002415276,0.000310317,0.0001103651,0.00009001687,0.00001264264,0.0003503731,0.0002790398,0.00001978372],"category_scores_gemma":[0.0001782495,0.0002348725,0.00008365904,0.0001531956,0.0002505316,0.00000369365,0.0004122366,0.0001036373,0.00000538371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003059533,"about_ca_system_score_gemma":0.0001651639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001205259,"about_ca_topic_score_gemma":0.0000466313,"domain_scores_codex":[0.9980868,0.0003150626,0.0004911128,0.0006897601,0.0001285777,0.0002887633],"domain_scores_gemma":[0.9990507,0.000063622,0.0002371978,0.0004108687,0.000176552,0.00006103788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001249763,0.00009861312,0.001444648,0.00001433376,0.00006691306,0.000001782791,0.0000144272,0.04705277,0.9456648,0.004708912,0.00007401783,0.0007338335],"study_design_scores_gemma":[0.005930231,0.003405185,0.3391432,0.00007563744,0.0001424536,0.00008266474,0.00006862784,0.4350665,0.08581245,0.1201563,0.008302646,0.001814189],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628036,0.0007493886,0.1355476,0.0001209857,0.0001860067,0.0003186178,0.0001225545,0.000005110134,0.0001460299],"genre_scores_gemma":[0.9847918,0.00004933413,0.01272319,0.000261304,0.0001071241,0.00002535952,0.002003453,0.00002208365,0.0000164017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8598523,"threshold_uncertainty_score":0.9577824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03123532041575599,"score_gpt":0.2842681028811567,"score_spread":0.2530327824654007,"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."}}