{"id":"W4413812274","doi":"10.1093/gigascience/giaf089","title":"WaveSeekerNet: accurate prediction of influenza A virus subtypes and host source using attention-based deep learning","year":2025,"lang":"en","type":"article","venue":"GigaScience","topic":"Influenza Virus Research Studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Guelph; Canadian Science Centre for Human and Animal Health; Vector Institute; University of Manitoba; Canadian Food Inspection Agency","funders":"Canadian Bee Research Fund; University of Manitoba; Natural Sciences and Engineering Research Council of Canada; Government of Canada; Canadian Institute for Advanced Research","keywords":"Host (biology); Computer science; Artificial intelligence; Generalization; Transmission (telecommunications); Adaptation (eye); Influenza A virus; Machine learning; Deep learning; Computational biology; Biology; Virus; Virology; Genetics","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":[0.0006869717,0.001192721,0.000635497,0.0008901512,0.0003403422,0.0006541278,0.001646619,0.0009486344,0.001392512],"category_scores_gemma":[0.001605347,0.0004365657,0.000740584,0.0004284751,0.000282581,0.001328198,0.001116162,0.00142266,0.0006424201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009182128,"about_ca_system_score_gemma":0.001266729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01548024,"about_ca_topic_score_gemma":0.02409689,"domain_scores_codex":[0.9997955,0.00003186177,0.00001284872,0.00008063739,0.00003810926,0.00004099084],"domain_scores_gemma":[0.9995204,0.0002315437,0.00004751854,0.00005401652,0.0001016793,0.00004486685],"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":[0.0006533625,0.00058614,0.02783353,0.0001910482,0.0003676367,0.0003169761,0.00009945344,0.6290015,0.009478581,0.002549708,0.03120102,0.2977211],"study_design_scores_gemma":[0.000009207731,0.00002078117,0.0003828301,0.00000429585,0.000008543815,0.00001239676,0.00000587144,0.9974965,0.0008304887,0.0009265411,0.000297431,0.000004959994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4299824,0.003018614,0.5348948,0.001505904,0.0004485153,0.0002438715,0.006757142,0.01795587,0.005192987],"genre_scores_gemma":[0.8760318,0.0005124327,0.1015733,0.0007642902,0.0001389764,0.0001651196,0.01310405,0.0002920231,0.007418017],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01548024,"threshold_uncertainty_score":0.03078032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05938065804533212,"score_gpt":0.3606568457651514,"score_spread":0.3012761877198193,"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."}}