{"id":"W4387951177","doi":"10.1109/ccece58730.2023.10288894","title":"STACKION: Ion Channel-Modulating Peptides Identification Using Stacking-Based Ensemble Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Peptide; Feature extraction; Computer science; Pseudo amino acid composition; Chemistry; Machine learning; Computational biology; Dipeptide; Biochemistry; Biology","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":[0.00107762,0.001259665,0.001205187,0.00135959,0.0003703993,0.0005808258,0.0008501582,0.0007199932,0.0008155777],"category_scores_gemma":[0.001419313,0.0002682564,0.001022685,0.0009762257,0.0002199848,0.001400638,0.0007997371,0.001011921,0.000447615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002950674,"about_ca_system_score_gemma":0.0006182442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001700358,"about_ca_topic_score_gemma":0.002426397,"domain_scores_codex":[0.9995198,0.00008876247,0.00003168019,0.0001345507,0.0001501655,0.0000750243],"domain_scores_gemma":[0.9994372,0.0002187906,0.00007959115,0.00007192992,0.0001514972,0.00004094404],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004313627,0.0003071757,0.01076916,0.0001627906,0.0005006334,0.0002583012,0.00009809888,0.1246156,0.05816672,0.001292931,0.005291113,0.7981061],"study_design_scores_gemma":[0.00001582777,0.0001404403,0.001865399,0.000007176352,0.00007388308,0.000115161,0.00001753117,0.9807972,0.01451032,0.001162213,0.001264402,0.00003063623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1402701,0.001709624,0.8498313,0.0002279284,0.0001420926,0.0001110096,0.0005179161,0.005334619,0.001855433],"genre_scores_gemma":[0.6383722,0.0007852121,0.3551781,0.000334204,0.0001477723,0.0001809008,0.00240381,0.0001592392,0.002438584],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001700358,"threshold_uncertainty_score":0.005699098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351674219221592,"score_gpt":0.2886698406946826,"score_spread":0.2651530985024667,"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."}}