{"id":"W2487715179","doi":"10.1016/j.ympev.2016.07.016","title":"An evaluation of logic regression-based biomarker discovery across multiple intergenic regions for predicting host specificity in Escherichia coli","year":2016,"lang":"en","type":"article","venue":"Molecular Phylogenetics and Evolution","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Provincial Laboratory of Public Health; Agriculture and Agri-Food Canada; Environment and Climate Change Canada; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biology; Intergenic region; Genetics; Computational biology; Genome; Host (biology); Host adaptation; In silico; Biomarker; Gene","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002659307,0.0007144096,0.0006002036,0.0007690793,0.0002344503,0.0007801726,0.0007692749,0.0006672299,0.0006672986],"category_scores_gemma":[0.003567203,0.0002183594,0.0004934448,0.0005397085,0.0002563679,0.000524158,0.0003967679,0.000447218,0.000206939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005201106,"about_ca_system_score_gemma":0.0007223082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002304631,"about_ca_topic_score_gemma":0.002055541,"domain_scores_codex":[0.9990313,0.0003746905,0.0000666964,0.0002475365,0.0001987755,0.00008105135],"domain_scores_gemma":[0.9974431,0.001768325,0.0002236956,0.00006578544,0.0004339803,0.00006519742],"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":[0.006462884,0.00121478,0.04120887,0.0003568403,0.0004308177,0.0003211519,0.00007126394,0.322106,0.3334705,0.00141142,0.0006353576,0.2923101],"study_design_scores_gemma":[0.00005563585,0.0006269407,0.002777582,0.000004680009,0.00006867229,0.00005810908,0.00002138364,0.9545122,0.041434,0.0002672781,0.0001605729,0.00001303823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8508134,0.000830101,0.145728,0.0002401472,0.00003359774,0.00006483147,0.0003435538,0.001052063,0.0008944983],"genre_scores_gemma":[0.9300382,0.000185606,0.06871686,0.00008812076,0.00001538005,0.0000331145,0.0004114071,0.00002643973,0.0004847759],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002659307,"threshold_uncertainty_score":0.01406389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02805532045612803,"score_gpt":0.29242312816333,"score_spread":0.264367807707202,"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."}}