{"id":"W4226148785","doi":"10.1186/s12864-022-08310-4","title":"AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens","year":2022,"lang":"en","type":"article","venue":"BMC Genomics","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":207,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; University of Victoria; Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Centre for Disease Control; BC Cancer Agency","funders":"National Human Genome Research Institute; National Institute of Allergy and Infectious Diseases; British Columbia Centre for Disease Control; Genome British Columbia; Genome Canada","keywords":"In silico; Antimicrobial peptides; Biology; Computational biology; Antibiotic resistance; Antimicrobial; Antibiotics; Artificial intelligence; Microbiology; Genetics; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006583852,0.0008266856,0.000593287,0.0003751349,0.0002051428,0.0005255365,0.001119214,0.001184313,0.00224665],"category_scores_gemma":[0.00107488,0.0002986345,0.0006403719,0.0002368264,0.0003565786,0.0004881635,0.0006366118,0.001174818,0.0003691393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006770993,"about_ca_system_score_gemma":0.0009282929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004408251,"about_ca_topic_score_gemma":0.004517439,"domain_scores_codex":[0.9998806,0.00003201456,0.000007268754,0.00003203462,0.00002220002,0.00002590587],"domain_scores_gemma":[0.9996579,0.0002047974,0.00002810316,0.0000125338,0.00006771735,0.00002897844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002738991,0.0002150205,0.003393139,0.0001450452,0.0001105986,0.0001188127,0.00003258054,0.9064273,0.005457783,0.002164922,0.004050742,0.07761021],"study_design_scores_gemma":[0.000009363886,0.00004179763,0.0000751838,0.000004520803,0.000008713471,0.000006886543,0.000002286819,0.9983832,0.0005344973,0.0007252793,0.0002059998,0.000002223524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2804248,0.003926858,0.6989135,0.002523206,0.0003311134,0.0001928185,0.001104711,0.005091861,0.00749112],"genre_scores_gemma":[0.8774408,0.0008091718,0.1125206,0.001105423,0.0001040755,0.0002690528,0.001458212,0.00009712696,0.006195631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004408251,"threshold_uncertainty_score":0.008765161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01837394947019517,"score_gpt":0.2323769141491792,"score_spread":0.2140029646789841,"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."}}