{"id":"W4319341254","doi":"10.1093/bioadv/vbad010","title":"NSPA: characterizing the disease association of multiple genetic interactions at single-subject resolution","year":2023,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Association (psychology); Subject (documents); Resolution (logic); Genetic association; Computational biology; Computer science; Biology; Genetics; Artificial intelligence; Psychology; Genotype; Single-nucleotide polymorphism; Gene; Library science","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":[0.0003374974,0.000105763,0.0001313845,0.0000564009,0.0002078183,0.00001385907,0.0001394149,0.00006756686,0.000008739394],"category_scores_gemma":[0.001056857,0.0000832594,0.0000983506,0.0001801001,0.0000472313,0.00001418701,0.0001292922,0.00005732339,0.00004998405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007562394,"about_ca_system_score_gemma":0.0000349321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006666871,"about_ca_topic_score_gemma":0.0001088067,"domain_scores_codex":[0.9990138,0.00007515046,0.0004051513,0.000123223,0.0001404781,0.0002421482],"domain_scores_gemma":[0.9989733,0.0001593058,0.0004624793,0.0002488689,0.0001043633,0.00005171773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002033888,0.0001464433,0.7325686,0.0001999068,0.0002617791,0.000001540217,0.0009755137,0.01687006,0.2046255,0.00006940438,0.02285399,0.02122392],"study_design_scores_gemma":[0.0005106861,0.0001595911,0.7182747,0.00003821655,0.00006660228,0.000004359952,0.0005322896,0.04396134,0.008327866,0.000172075,0.2276843,0.0002679485],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946809,0.0004833268,0.002493256,0.0009559945,0.0004540328,0.0002235629,0.0001115196,0.0000300241,0.0005673825],"genre_scores_gemma":[0.9937542,0.001168098,0.003105524,0.0002019769,0.0001596528,0.00004451968,0.0003993977,0.0000119724,0.001154631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2048304,"threshold_uncertainty_score":0.339522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01775615102493194,"score_gpt":0.2633413426690291,"score_spread":0.2455851916440972,"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."}}