{"id":"W2128495797","doi":"10.1093/bioinformatics/btm068","title":"AMPer: a database and an automated discovery tool for antimicrobial peptides","year":2007,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":233,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Antimicrobial peptides; Hidden Markov model; Construct (python library); Computer science; Computational biology; Biological database; Database; Biology; Artificial intelligence; Bioinformatics; Peptide; Biochemistry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003828576,0.0002009461,0.000251061,0.0001081552,0.0003419746,0.0001116975,0.0001432333,0.0001724956,0.00001647321],"category_scores_gemma":[0.0000614757,0.0001712385,0.00007032677,0.00006910542,0.0003088142,0.001195294,0.00009998705,0.0001242205,0.00005122641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000209048,"about_ca_system_score_gemma":0.00003893613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000504918,"about_ca_topic_score_gemma":0.00004343923,"domain_scores_codex":[0.9989491,0.00001742837,0.0004088673,0.0001584265,0.00002561921,0.0004405712],"domain_scores_gemma":[0.9993911,0.000142365,0.0001466377,0.0002393861,0.00005133538,0.00002915387],"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":[0.0005779547,0.0002404431,0.001681968,0.0004417316,0.0001724959,0.000004705801,0.002011348,0.000008658489,0.9213774,0.00417725,0.05814933,0.01115676],"study_design_scores_gemma":[0.007344036,0.001234629,0.007274729,0.0004014993,0.0002848738,0.0006664411,0.006412941,0.003627901,0.8870922,0.0001759188,0.08349174,0.001993069],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9738142,0.0001002518,0.02357244,0.00006145955,0.0004461316,0.0004579406,0.0007716401,0.0003459489,0.0004299453],"genre_scores_gemma":[0.9732068,0.00007076665,0.02379223,0.0006451214,0.00008801903,0.000006452937,0.001128944,0.00002394528,0.001037725],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03428514,"threshold_uncertainty_score":0.6982902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418059534551134,"score_gpt":0.2691789252803353,"score_spread":0.254998329934824,"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."}}