{"id":"W4408433527","doi":"10.1021/acs.jcim.4c01749","title":"BERT-AmPEP60: A BERT-Based Transfer Learning Approach to Predict the Minimum Inhibitory Concentrations of Antimicrobial Peptides for <i>Escherichia coli</i> and <i>Staphylococcus aureus</i>","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Infection and Immunity; University of Ottawa","funders":"China Postdoctoral Science Foundation; Government of Canada","keywords":"Escherichia coli; Staphylococcus aureus; Minimum inhibitory concentration; Antimicrobial; Microbiology; Chemistry; Inhibitory postsynaptic potential; Biology; Bacteria; Biochemistry; Genetics; 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":[0.0005190935,0.0009387875,0.0004586707,0.0003814766,0.000146197,0.0003487194,0.0009305102,0.0008134763,0.001344736],"category_scores_gemma":[0.001101875,0.0002883254,0.000444556,0.0002481059,0.00025073,0.0008101391,0.0005284822,0.001150913,0.0006361133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004952326,"about_ca_system_score_gemma":0.0007192238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002469095,"about_ca_topic_score_gemma":0.003341781,"domain_scores_codex":[0.9998471,0.00002667686,0.000008488822,0.00004606975,0.0000484459,0.00002329279],"domain_scores_gemma":[0.9997575,0.00009165631,0.00003569466,0.00002175924,0.00007235214,0.00002105188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003416859,0.0006205306,0.005676387,0.0001895257,0.0001200065,0.0001810223,0.00006518719,0.4932409,0.1144907,0.003409023,0.00414727,0.3775178],"study_design_scores_gemma":[0.000005630022,0.0001145466,0.0003130028,0.000003812986,0.000006740382,0.0000254718,0.000004125385,0.985692,0.01282401,0.0005486018,0.0004531696,0.000008939115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2254769,0.001114982,0.7628244,0.0004227552,0.0001283165,0.0001556182,0.0005088631,0.00542557,0.003942578],"genre_scores_gemma":[0.8062275,0.0005218977,0.1837679,0.0003536119,0.00004298182,0.0002053415,0.001353866,0.0001519649,0.007374978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002469095,"threshold_uncertainty_score":0.004909515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112398835598922,"score_gpt":0.2238366177694291,"score_spread":0.2125967342095369,"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."}}