{"id":"W3080687441","doi":"10.1007/s12021-020-09486-4","title":"Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies","year":2020,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Mental Health; Canadian Institutes of Health Research; National Institute on Aging; National Institutes of Health","keywords":"Neuroimaging; Phenome; Imaging genetics; Computer science; Genome-wide association study; Biobank; Genetic association; Connectomics; Bioinformatics; Connectome; Biology; Neuroscience; Phenotype; Genetics; Single-nucleotide polymorphism; Genotype; Gene","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001810301,0.0003349926,0.0005279998,0.00007330385,0.0004183272,0.0001276366,0.0007530221,0.0001490643,0.000002205879],"category_scores_gemma":[0.0628997,0.0003519185,0.0001612506,0.0002345677,0.00002853883,0.00004823453,0.0009475756,0.0002934097,0.00002104491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000205121,"about_ca_system_score_gemma":0.0001322309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000140049,"about_ca_topic_score_gemma":0.00004189811,"domain_scores_codex":[0.9971355,0.0001684777,0.001065604,0.0005243853,0.00039955,0.0007064485],"domain_scores_gemma":[0.9940991,0.003393902,0.001186595,0.0007468741,0.0004254784,0.0001480043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003970905,0.000075474,0.5715587,0.0000965747,0.0003425454,0.000002249114,0.002408626,0.001462875,0.0004726037,0.00001025222,0.4200797,0.00345066],"study_design_scores_gemma":[0.01189775,0.002114131,0.2482238,0.0000755273,0.000938891,0.00001246321,0.02044365,0.1875661,0.0009016461,0.001494198,0.5240924,0.002239462],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9267201,0.0004270794,0.02539795,0.04250417,0.002287798,0.00150687,0.0002642631,0.000149782,0.0007419221],"genre_scores_gemma":[0.9413761,0.0001801255,0.006012408,0.04855901,0.002661033,0.00004464606,0.0006768522,0.00009023899,0.000399552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3233349,"threshold_uncertainty_score":0.9998933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05763992541250098,"score_gpt":0.3002576370425839,"score_spread":0.2426177116300829,"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."}}