{"id":"W4381187244","doi":"10.1002/sim.9824","title":"Deep causal feature extraction and inference with neuroimaging genetic data","year":2023,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of General Medical Sciences; National Institute on Aging; Canadian Institutes of Health Research; Minnesota Supercomputing Institute, University of Minnesota; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Pfizer; Novartis Pharmaceuticals Corporation; Eisai; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Servier; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Biogen","keywords":"Neuroimaging; Computer science; Causal inference; Imaging genetics; Artificial intelligence; Covariate; Inference; Genome-wide association study; Biobank; Alzheimer's Disease Neuroimaging Initiative; Instrumental variable; Machine learning; Feature (linguistics); Regression; Psychology; Econometrics; Cognition; Bioinformatics; Statistics; Cognitive impairment; Psychiatry; Biology; Mathematics","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":[0.004492625,0.001022461,0.00136107,0.001720573,0.0004822033,0.001134194,0.001727375,0.001186921,0.001877908],"category_scores_gemma":[0.01971643,0.000664414,0.001936886,0.001519627,0.0009694748,0.001147061,0.001557211,0.002791962,0.0003866345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055746,"about_ca_system_score_gemma":0.001810787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009481919,"about_ca_topic_score_gemma":0.01020962,"domain_scores_codex":[0.9981897,0.000814212,0.0001305322,0.0005494537,0.0001826848,0.0001333832],"domain_scores_gemma":[0.9893231,0.00877568,0.0006363341,0.0007482368,0.0003697054,0.0001469078],"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":[0.0005060943,0.0002787674,0.03363254,0.0004043212,0.00110332,0.001159374,0.0002660255,0.5406762,0.004293592,0.05660349,0.009293536,0.3517828],"study_design_scores_gemma":[0.0000524105,0.00003605489,0.002103126,0.00003559758,0.00008200887,0.0001019338,0.0000201741,0.9056044,0.001071158,0.08937415,0.001499277,0.00001982284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0216176,0.0007266659,0.9740326,0.001061448,0.00004911087,0.00004253147,0.0008896404,0.001178985,0.0004014152],"genre_scores_gemma":[0.6488214,0.001070812,0.3410986,0.001065339,0.0002922243,0.0002745377,0.004721091,0.0002034579,0.002452584],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009481919,"threshold_uncertainty_score":0.02375954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03034836853606217,"score_gpt":0.3566172819314706,"score_spread":0.3262689133954084,"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."}}