{"id":"W4400228131","doi":"10.1109/tai.2024.3421176","title":"StackAMP: Stacking-Based Ensemble Classifier for Antimicrobial Peptide Identification","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Classifier (UML); Stacking; Identification (biology); Artificial intelligence; Antimicrobial; Computational biology; Computer science; Ensemble learning; Machine learning; Pattern recognition (psychology); Biology; Chemistry; Microbiology","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":[0.0003177014,0.0001921034,0.0001223301,0.0001489958,0.0002221473,0.0002051974,0.000203159,0.0001662852,0.00007071296],"category_scores_gemma":[0.00003835772,0.0001974258,0.0001917112,0.0002067474,0.0001024616,0.00001577311,0.000001935352,0.0002225023,0.0002162217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004133855,"about_ca_system_score_gemma":0.0001370343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001240271,"about_ca_topic_score_gemma":0.0001140878,"domain_scores_codex":[0.998678,0.00004484069,0.0004544134,0.0003890238,0.0001601457,0.0002735765],"domain_scores_gemma":[0.9992881,0.00008397446,0.00007119032,0.0003599201,0.0001285287,0.00006831407],"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":[0.0002715016,0.0001838319,0.000003371722,0.0001928796,0.00007124277,0.00000207263,0.0002104189,0.1502065,0.7201477,0.001062542,0.002389692,0.1252583],"study_design_scores_gemma":[0.00003207637,0.0001961192,0.000003333307,0.00004613526,0.00003127566,0.000004033789,0.00006092617,0.1786266,0.8062704,0.0003018789,0.01423048,0.0001967661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02964334,0.00003153721,0.9676479,0.00043784,0.001349768,0.0003933311,0.0001265733,0.00009843756,0.0002713063],"genre_scores_gemma":[0.9926149,0.00002841079,0.00537258,0.0002886867,0.0001897158,0.00007620636,0.00008686836,0.00004248826,0.001300146],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9629716,"threshold_uncertainty_score":0.8050791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03365630141997564,"score_gpt":0.3175476373886711,"score_spread":0.2838913359686954,"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."}}