{"id":"W7126031106","doi":"10.1109/bibm66473.2025.11356356","title":"Benchmarking Bayesian Deep Learning (BDL) for Important SNP Identification in Plant Genomes","year":2025,"lang":"","type":"article","venue":"","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; University of Guelph","keywords":"Interpretability; Deep learning; Benchmarking; Convolutional neural network; Bayesian probability; Identification (biology); Bayesian network; Genome-wide association study; Feature (linguistics)","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":[0.007019373,0.001114009,0.0007490634,0.001618506,0.0005997236,0.001461029,0.002138256,0.001837413,0.004704412],"category_scores_gemma":[0.02092797,0.0006335009,0.001039279,0.001481828,0.0006799664,0.001641382,0.002032726,0.001912651,0.001641522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00180295,"about_ca_system_score_gemma":0.002448926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01279497,"about_ca_topic_score_gemma":0.02104865,"domain_scores_codex":[0.9975694,0.001155843,0.0001402675,0.0004557934,0.0005091934,0.0001694535],"domain_scores_gemma":[0.9940591,0.004015865,0.000224983,0.000839658,0.0006876382,0.0001727436],"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":[0.0009615161,0.0005121493,0.03161927,0.0009864285,0.0007596312,0.000190836,0.0001548773,0.6515118,0.006068412,0.02265956,0.03220395,0.2523715],"study_design_scores_gemma":[0.00007870887,0.00006171776,0.001678422,0.00004534844,0.00002732349,0.00004039062,0.00002585172,0.9779685,0.002292758,0.0148459,0.002917748,0.00001729598],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2361082,0.003804937,0.6948881,0.002498349,0.000380115,0.000319878,0.01776667,0.03291314,0.01132065],"genre_scores_gemma":[0.5702446,0.0007018094,0.394437,0.001096896,0.0000671054,0.0004231823,0.02837142,0.001593808,0.003064175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01279497,"threshold_uncertainty_score":0.03712243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088327361452631,"score_gpt":0.276985753520587,"score_spread":0.2661024799060607,"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."}}