{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003440966,0.0006975882,0.0007729937,0.00188296,0.0006181184,0.001004194,0.001048664,0.0008771226,0.008351387],"category_scores_gemma":[0.01124045,0.0002891248,0.001295571,0.001812914,0.0006633113,0.0008164129,0.001145626,0.001136199,0.00152708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005549005,"about_ca_system_score_gemma":0.0009367485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005003939,"about_ca_topic_score_gemma":0.006663472,"domain_scores_codex":[0.9988909,0.0004815071,0.00004822504,0.0003894681,0.0001486078,0.00004124521],"domain_scores_gemma":[0.9929357,0.005020401,0.0006384421,0.0008061988,0.0003312763,0.0002679444],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001461285,0.0004743554,0.3227488,0.00144372,0.001885194,0.002060543,0.0009565908,0.2161795,0.01777155,0.04768876,0.0928199,0.2945098],"study_design_scores_gemma":[0.0001945309,0.0002936928,0.09025777,0.0001577894,0.0005043321,0.001512895,0.0002234012,0.6815426,0.004138328,0.186373,0.03470636,0.00009529583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2219614,0.002000104,0.7087111,0.003657974,0.0002942407,0.0003317168,0.04990759,0.004681157,0.008454577],"genre_scores_gemma":[0.7620578,0.0008831113,0.1918163,0.0005541579,0.0003473631,0.0006225915,0.03894585,0.0003689661,0.004403759],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008351387,"threshold_uncertainty_score":0.02793819,"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."}}