{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002839926,0.0001262536,0.0002793137,0.00009684379,0.0001645836,0.00004723046,0.000100354,0.0001402991,0.000001867343],"category_scores_gemma":[0.00008152724,0.00009291414,0.0001111227,0.00008795498,0.000161239,0.0003183083,0.00002726194,0.0002887383,3.206065e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001666578,"about_ca_system_score_gemma":0.0001301868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001176692,"about_ca_topic_score_gemma":7.322799e-7,"domain_scores_codex":[0.9990727,0.00003651891,0.000606022,0.00007816525,0.00004158083,0.0001650061],"domain_scores_gemma":[0.9993857,0.0001424906,0.0001549093,0.00005912253,0.0002270059,0.00003077738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004563314,0.00005578635,0.00007451585,0.000160275,0.00008721869,4.25826e-8,0.001413751,0.01310014,0.9807653,0.0003980384,0.002940536,0.0005480683],"study_design_scores_gemma":[0.003083385,0.0001504724,0.00001741471,0.0003448049,0.0001686877,0.00001155065,0.00197688,0.03383827,0.951467,0.00005096659,0.008703313,0.0001872818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8774191,0.0002647895,0.1209738,0.0006827235,0.0001059835,0.0002696301,0.00003175046,0.000009514136,0.0002427659],"genre_scores_gemma":[0.9969214,0.00006587226,0.001491002,0.001404927,0.00003332295,0.000008338379,0.00003401559,0.000005271238,0.00003586071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1195023,"threshold_uncertainty_score":0.3788928,"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."}}