{"id":"W2767946998","doi":"10.1016/j.vetimm.2017.11.004","title":"In silico identification and high throughput screening of antigenic proteins as candidates for a Mannheimia haemolytica vaccine","year":2017,"lang":"en","type":"article","venue":"Veterinary Immunology and Immunopathology","topic":"Microbial infections and disease research","field":"Immunology and Microbiology","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Agriculture and Agri-Food Canada","funders":"Alberta Livestock and Meat Agency","keywords":"Biology; Immunoscreening; Bacterial outer membrane; Periplasmic space; ATP-binding cassette transporter; Signal peptide; Microbiology; Antigen; Membrane protein; In silico; Immunogenicity; Maltose-binding protein; Virology; Molecular biology; Fusion protein; Peptide sequence; cDNA library; Biochemistry; Transporter; Escherichia coli; Gene; Genetics","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":[0.000568455,0.0008413859,0.000993751,0.0006411016,0.0004057485,0.0009325703,0.0005195866,0.0005201467,0.001623284],"category_scores_gemma":[0.0008159773,0.0003995778,0.001398176,0.0005006426,0.0001623776,0.0003379037,0.0003862057,0.0005594851,0.0006100376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004349503,"about_ca_system_score_gemma":0.0006124954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000859025,"about_ca_topic_score_gemma":0.001666232,"domain_scores_codex":[0.9997253,0.00006773639,0.0000159546,0.00005480748,0.00008280585,0.00005331897],"domain_scores_gemma":[0.9997004,0.0001776726,0.00004407829,0.00001808595,0.00003503286,0.00002469613],"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":[0.003107445,0.001737834,0.02700551,0.001167333,0.0006281803,0.001313248,0.000114923,0.1215789,0.8112487,0.001107219,0.001608386,0.02938246],"study_design_scores_gemma":[0.0003420594,0.004100913,0.01917347,0.00005436474,0.001296005,0.001137255,0.0002537792,0.5311867,0.4347825,0.0009588589,0.006649145,0.00006484741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9851539,0.0008811629,0.01082979,0.0001533743,0.00002803567,0.00009987732,0.001494687,0.0003171127,0.001042043],"genre_scores_gemma":[0.9704264,0.000713532,0.02308964,0.00005743195,0.00001163154,0.00007836575,0.004638375,0.00004377809,0.0009408515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001623284,"threshold_uncertainty_score":0.0054304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02523711462354218,"score_gpt":0.3180844680969334,"score_spread":0.2928473534733912,"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."}}