{"id":"W4385351582","doi":"10.1016/j.chom.2023.07.001","title":"Molecular de-extinction of ancient antimicrobial peptides enabled by machine learning","year":2023,"lang":"en","type":"article","venue":"Cell Host & Microbe","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":145,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Perelman School of Medicine, University of Pennsylvania; Defense Threat Reduction Agency; University of Pennsylvania; Beef Cattle Research Council; ACE Foundation; Innovative Research Group Project of the National Natural Science Foundation of China; University of Texas at Austin; Brain and Behavior Research Foundation; National Institute of General Medical Sciences; United Therapeutics Corporation; National Institutes of Health; Procter and Gamble; Alfred P. Sloan Foundation","keywords":"Biology; Antimicrobial; Proteome; Protease; Antimicrobial peptides; Extant taxon; Drug discovery; Peptide; Computational biology; Biochemistry; Microbiology; Evolutionary biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004624545,0.0001871105,0.0002522887,0.0001517389,0.0001698828,0.0006663021,0.0004071405,0.0004703939,0.001118994],"category_scores_gemma":[0.0007079,0.0001410194,0.0001965937,0.0001663167,0.0006307131,0.0008945973,0.0004321513,0.001152176,0.0002364799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003889346,"about_ca_system_score_gemma":0.000182118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001215514,"about_ca_topic_score_gemma":0.0001828482,"domain_scores_codex":[0.9998832,0.00002140935,0.000006868121,0.00003670546,0.00002562315,0.00002616196],"domain_scores_gemma":[0.9997577,0.0000849411,0.00006204593,0.00005110295,0.00002327797,0.00002094199],"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":[0.0003338438,0.00009858588,0.003500954,0.0002998204,0.00006613677,0.0003099837,0.0001260125,0.01498229,0.8334774,0.03288678,0.0003803734,0.1135379],"study_design_scores_gemma":[0.00007707006,0.000737256,0.01010922,0.00008748988,0.00007368939,0.001121622,0.0001432968,0.1519832,0.7613449,0.04697873,0.02727949,0.00006407699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9115469,0.006515139,0.07398949,0.0009441917,0.0002430988,0.00002353451,0.00007614326,0.000258787,0.006402786],"genre_scores_gemma":[0.9829258,0.001497554,0.01400208,0.0001870991,0.00003271456,0.00001091547,0.00005260966,0.00002206774,0.00126919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001118994,"threshold_uncertainty_score":0.00374341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005779273999038274,"score_gpt":0.2044380794193067,"score_spread":0.1986588054202684,"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."}}