{"id":"W2507765028","doi":"10.1186/s12920-016-0190-9","title":"Integration of bioinformatics and imaging informatics for identifying rare PSEN1 variants in Alzheimer’s disease","year":2016,"lang":"en","type":"article","venue":"BMC Medical Genomics","topic":"Alzheimer's disease research and treatments","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; U.S. National Library of Medicine; IXICO; Eisai; Servier; Brin Wojcicki Foundation; Northern California Institute for Research and Education; University of California, San Diego; Pfizer; Biogen; BioClinica; F. Hoffmann-La Roche; Synarc; University of Southern California; Medpace; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; National Center for Advancing Translational Sciences; Meso Scale Diagnostics; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"PSEN1; Alzheimer's Disease Neuroimaging Initiative; Endophenotype; Neuroimaging; Minor allele frequency; Genome-wide association study; Genetic association; Dementia; Bioinformatics; Genetics; Allele; Biology; Medicine; Alzheimer's disease; Allele frequency; Disease; Pathology; Gene; Single-nucleotide polymorphism; Neuroscience; Presenilin; Genotype","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":[0.003663742,0.0007757195,0.000837619,0.00409293,0.0004612779,0.001169686,0.0005775248,0.0003569594,0.001107541],"category_scores_gemma":[0.007486357,0.000391042,0.001088444,0.003308001,0.0003095867,0.0006196287,0.001321124,0.000515386,0.0006269155],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004013519,"about_ca_system_score_gemma":0.001292751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003130471,"about_ca_topic_score_gemma":0.006147676,"domain_scores_codex":[0.9984155,0.0007440049,0.0001425977,0.0003493856,0.0002694971,0.00007904662],"domain_scores_gemma":[0.995968,0.002604873,0.0004772769,0.0003803387,0.0003765133,0.0001929773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001778887,0.0007686662,0.4696678,0.0008304683,0.001883411,0.001268291,0.0006010777,0.05906792,0.03410079,0.003375764,0.01015639,0.4165006],"study_design_scores_gemma":[0.0002567085,0.0004563425,0.2814225,0.0001458392,0.0008516787,0.001056302,0.0004144537,0.6727098,0.01551695,0.01711382,0.009951869,0.0001038295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5604084,0.002084094,0.4036396,0.001723661,0.00007898201,0.0004995393,0.01304661,0.0147053,0.00381388],"genre_scores_gemma":[0.5484887,0.0008371142,0.4339951,0.0002541833,0.00009719639,0.0003943567,0.01487838,0.0005083269,0.0005466345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00409293,"threshold_uncertainty_score":0.01937598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06025153453471344,"score_gpt":0.3462086101157711,"score_spread":0.2859570755810577,"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."}}