{"id":"W4391021954","doi":"10.1371/journal.pone.0296627","title":"Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; Université du Québec à Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Discriminative model; Computational biology; Genome; Context (archaeology); Genomics; Biology; Phylogenetic tree; Genetics; Computer science; Artificial intelligence; 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":[0.0008622791,0.0008538858,0.001060697,0.002002273,0.0004643963,0.001145811,0.001134497,0.001087935,0.002107951],"category_scores_gemma":[0.002590578,0.0003443297,0.001144957,0.0008731385,0.0003572294,0.001117972,0.001134902,0.001107566,0.001135362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005160683,"about_ca_system_score_gemma":0.0008147967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001678764,"about_ca_topic_score_gemma":0.003377388,"domain_scores_codex":[0.9994909,0.00008728069,0.00003537642,0.0002008389,0.0001155153,0.0000700182],"domain_scores_gemma":[0.9994043,0.0002726837,0.00005694351,0.00008987448,0.0001482546,0.00002802148],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001030434,0.0004669005,0.02401999,0.0004211641,0.0004691657,0.0003792001,0.0002395271,0.162509,0.07729004,0.00703663,0.01254946,0.7135884],"study_design_scores_gemma":[0.00002615432,0.00009872591,0.002741905,0.00001437711,0.00003399009,0.000213789,0.00005535192,0.967804,0.02073746,0.004217873,0.004016866,0.00003953417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2285511,0.0006959297,0.7459323,0.0003433671,0.000110426,0.0001794932,0.002208583,0.01928183,0.002697014],"genre_scores_gemma":[0.4822701,0.0002472646,0.506114,0.000273992,0.00003585207,0.000254825,0.005806018,0.0009367252,0.004061261],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002107951,"threshold_uncertainty_score":0.007051766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0703698993668386,"score_gpt":0.3004134790456081,"score_spread":0.2300435796787695,"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."}}