{"id":"W4391353343","doi":"10.1186/s13040-024-00355-3","title":"Revealing third-order interactions through the integration of machine learning and entropy methods in genomic studies","year":2024,"lang":"en","type":"article","venue":"BioData Mining","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Canadian Institutes of Health Research; Genentech; IXICO; H. Lundbeck A/S; Servier; National Institutes of Health; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Eisai; Pfizer; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Meso Scale Diagnostics; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Biogen","keywords":"Genome-wide association study; Computer science; Epistasis; Single-nucleotide polymorphism; Genetic association; SNP; Computational biology; Artificial intelligence; Data mining; Machine learning; Biology; Genetics; Genotype; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004751988,0.0009995464,0.0009977149,0.003199466,0.0005365888,0.001416312,0.0009349341,0.0005932856,0.001225916],"category_scores_gemma":[0.009189226,0.0003515731,0.002625103,0.001846935,0.0007328783,0.001085745,0.001565625,0.001172884,0.000315994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005602583,"about_ca_system_score_gemma":0.001145656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002529044,"about_ca_topic_score_gemma":0.002865958,"domain_scores_codex":[0.9979101,0.001122938,0.0001136124,0.0003581948,0.0003935227,0.0001016018],"domain_scores_gemma":[0.9922345,0.006231718,0.000507935,0.000528869,0.0003473309,0.0001497438],"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":[0.0005059151,0.0003707432,0.07380475,0.0006606018,0.001365098,0.001042186,0.0006348251,0.5083421,0.02719044,0.04282587,0.002417788,0.3408397],"study_design_scores_gemma":[0.00001597629,0.00006202018,0.009322106,0.00002640014,0.0001037,0.0001261832,0.00003865602,0.9427505,0.003532515,0.04255722,0.001415856,0.00004884711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03914229,0.0004638336,0.9583568,0.0001868154,0.00002398898,0.00005651937,0.0003275497,0.000790838,0.0006514255],"genre_scores_gemma":[0.500574,0.0005707212,0.4959712,0.0001617997,0.00008948391,0.0003221109,0.001405871,0.0002166762,0.0006880604],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004751988,"threshold_uncertainty_score":0.02513123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07409299352930675,"score_gpt":0.4222588802548448,"score_spread":0.348165886725538,"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."}}