{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005654279,0.0001172576,0.0002182831,0.0002913779,0.0003143292,0.00003879468,0.000108378,0.00005623139,0.00001605797],"category_scores_gemma":[0.001186395,0.0001023099,0.00004918674,0.0007354243,0.0005829858,0.0001733781,0.0001258291,0.0002202101,0.000005547394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008108024,"about_ca_system_score_gemma":0.0002082994,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002157264,"about_ca_topic_score_gemma":0.00002347537,"domain_scores_codex":[0.9986464,0.00006638165,0.000269348,0.0003026932,0.0004152227,0.000299974],"domain_scores_gemma":[0.9991976,0.0001518329,0.0001059666,0.0001897778,0.0002739114,0.00008092171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001816064,0.00005397139,0.48398,0.0002312813,0.00004394489,0.000005089864,0.0004816263,0.002394541,0.5085938,0.00007449924,0.00001880565,0.003940822],"study_design_scores_gemma":[0.001460082,0.0002787887,0.750385,0.0005078932,0.00008572727,0.00000697401,0.0005809556,0.2107326,0.03272255,0.00003101128,0.003088453,0.000119952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98841,0.001193972,0.00927373,0.0001071473,0.00006183747,0.0002726289,0.000009838628,0.00005247631,0.0006183811],"genre_scores_gemma":[0.9982104,0.00006699852,0.0009966812,0.0003528116,0.00002291873,0.00001064694,0.000001728503,0.000007935569,0.0003299174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4758712,"threshold_uncertainty_score":0.4172077,"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."}}