{"id":"W4232737019","doi":"10.1101/2020.06.16.155705","title":"AMPlify: attentive deep learning model for discovery of novel antimicrobial peptides effective against WHO priority pathogens","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Antimicrobial Peptides and Activities","field":"Immunology and Microbiology","cited_by":6,"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":"British Columbia Centre for Disease Control","keywords":"In silico; Antimicrobial peptides; Antibiotic resistance; Computational biology; Biology; Antimicrobial; Bullfrog; Antibiotics; Artificial intelligence; Microbiology; Computer science; Genetics; Gene; Ecology","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":[0.0007429042,0.0009085333,0.0005910835,0.0004238169,0.0001901425,0.0005354237,0.001097677,0.001258249,0.002424288],"category_scores_gemma":[0.001114696,0.0003258046,0.0006027481,0.0002374534,0.000338391,0.000470769,0.0006196998,0.001117373,0.000405967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006572987,"about_ca_system_score_gemma":0.0008738521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004366594,"about_ca_topic_score_gemma":0.004525011,"domain_scores_codex":[0.9998769,0.00003304685,0.000006558192,0.00003479881,0.00002237239,0.00002630193],"domain_scores_gemma":[0.9996704,0.0001914196,0.00002476189,0.00001430222,0.0000713015,0.00002787021],"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":[0.0002630919,0.0002005518,0.002823387,0.000105551,0.00009484434,0.00008840169,0.00002384874,0.9260145,0.00609585,0.001612927,0.003646054,0.05903098],"study_design_scores_gemma":[0.000007832569,0.000027788,0.00005407443,0.000002549847,0.000005549036,0.000004079528,0.000001657889,0.998812,0.0005558488,0.0004057869,0.000121152,0.00000177514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3513364,0.002632181,0.6289608,0.002096426,0.000288631,0.0002055062,0.001180855,0.005984984,0.007314218],"genre_scores_gemma":[0.8925611,0.0004698373,0.09871657,0.0008583638,0.00008991197,0.0002433186,0.00131298,0.000104014,0.005644014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004366594,"threshold_uncertainty_score":0.00868237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01595580568172833,"score_gpt":0.2229664841864582,"score_spread":0.2070106785047299,"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."}}