{"id":"W2738914580","doi":"10.1016/j.neuroimage.2017.07.030","title":"FGWAS: Functional genome wide association analysis","year":2017,"lang":"en","type":"article","venue":"NeuroImage","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Institute of Mental Health; National Institute on Aging; University of California, San Diego; National Institutes of Health; Servier; Eisai; Northern California Institute for Research and Education; Alzheimer's Association; Fujirebio US; Pfizer; BioClinica; Genentech; Cancer Prevention and Research Institute of Texas; Biogen Idec; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Takeda Pharmaceuticals North America; Novartis Pharmaceuticals Corporation; Synarc; University of Southern California; Roche; Merck; Alzheimer's Drug Discovery Foundation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Foundation for the National Institutes of Health; GE Healthcare; Alzheimer's Disease Neuroimaging Initiative; National Science Foundation","keywords":"Genome-wide association study; Multivariate statistics; Genetic association; Imaging genetics; Principal component analysis; Computer science; Functional principal component analysis; Computational biology; Independent component analysis; Neuroimaging; Biology; Artificial intelligence; Pattern recognition (psychology); Single-nucleotide polymorphism; Machine learning; Genetics; Neuroscience; 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":[0.006146813,0.002471712,0.002409214,0.006308551,0.001492785,0.002515027,0.003012626,0.001869025,0.03819768],"category_scores_gemma":[0.02283668,0.001408253,0.004110907,0.006083882,0.0007893404,0.001096677,0.003086829,0.001783848,0.003688775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004882465,"about_ca_system_score_gemma":0.002110692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005682229,"about_ca_topic_score_gemma":0.005812116,"domain_scores_codex":[0.99402,0.002482108,0.0004244875,0.002045406,0.0005706077,0.0004574794],"domain_scores_gemma":[0.9869907,0.009165196,0.0007475002,0.001927348,0.0006590334,0.0005101727],"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":[0.01739246,0.0008483693,0.3848627,0.004698956,0.05343378,0.007045095,0.001397237,0.01358338,0.03081401,0.02539635,0.201612,0.2589156],"study_design_scores_gemma":[0.005512067,0.002646374,0.484376,0.000887155,0.04772051,0.01164162,0.000941033,0.09541749,0.01952648,0.09527595,0.2351763,0.0008789496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2328036,0.003862014,0.4644501,0.002708378,0.001479969,0.0009148831,0.2434912,0.04288689,0.007403045],"genre_scores_gemma":[0.6250246,0.001183233,0.2582587,0.001460915,0.0006552402,0.004805107,0.08572475,0.008213833,0.01467373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03819768,"threshold_uncertainty_score":0.1277839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.019206547185002,"score_gpt":0.2666003352989578,"score_spread":0.2473937881139558,"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."}}