{"id":"W2890483294","doi":"10.1101/421719","title":"GenEpi: Gene-based Epistasis Discovery Using Machine Learning","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; Canadian Institutes of Health Research; Genentech; National Institutes of Health; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; Ministry of Science and Technology, Taiwan; Pfizer; Biogen; BioClinica; Medpace; Bristol-Myers Squibb; Eli Lilly and Company; AstraZeneca; Novartis Pharmaceuticals Corporation; Bayer HealthCare; Dana Foundation; Synarc; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Epistasis; Python (programming language); Genome-wide association study; Computer science; Computational biology; Machine learning; Discriminative model; Workflow; Artificial intelligence; Biology; Gene; Genetics; Single-nucleotide polymorphism; Genotype","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":[0.001720548,0.001385514,0.001405503,0.001054002,0.0004947227,0.001094787,0.002349459,0.0007496953,0.01454948],"category_scores_gemma":[0.005738158,0.0006067398,0.00171337,0.0009574477,0.000549961,0.0008426359,0.001488154,0.001862973,0.004431385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000451777,"about_ca_system_score_gemma":0.001907012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002542799,"about_ca_topic_score_gemma":0.003421671,"domain_scores_codex":[0.9994218,0.0002019744,0.00002568879,0.0001574916,0.0001454319,0.00004753434],"domain_scores_gemma":[0.9987773,0.0008499586,0.00009347578,0.0001274328,0.00008430534,0.00006749601],"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":[0.001432408,0.0005714109,0.02307451,0.002732775,0.002406423,0.001442676,0.0004298801,0.2791244,0.02340928,0.053481,0.3023095,0.3095858],"study_design_scores_gemma":[0.0002070817,0.00007605197,0.001951784,0.00004386963,0.0001177443,0.0001715815,0.00001679408,0.9430413,0.004640957,0.0282588,0.02141506,0.0000590032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01726963,0.0005812601,0.8249604,0.000726936,0.0002102976,0.0002144483,0.016378,0.1368222,0.002836849],"genre_scores_gemma":[0.1941445,0.0006177613,0.7550734,0.0008686535,0.0001396912,0.001523854,0.02895646,0.01252015,0.006155554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01454948,"threshold_uncertainty_score":0.04867291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01829991147790085,"score_gpt":0.2435169711938097,"score_spread":0.2252170597159088,"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."}}