{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000422307,0.0001355007,0.0002702576,0.0001779534,0.00004415242,0.00001926596,0.00009551555,0.00006356892,0.00005036855],"category_scores_gemma":[0.0009517319,0.00008776258,0.00007712736,0.00009336309,0.000152863,0.0002436465,0.00008405236,0.00007859938,0.00001400513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007439703,"about_ca_system_score_gemma":0.0008458876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001222865,"about_ca_topic_score_gemma":0.00002089632,"domain_scores_codex":[0.9985495,0.00002946277,0.0005954303,0.0001155461,0.0004246332,0.0002854204],"domain_scores_gemma":[0.9985679,0.0003558029,0.0001397542,0.0002172038,0.00009615832,0.0006231977],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004361008,0.001254538,0.4053798,0.003365966,0.003247678,0.0002043307,0.003310507,0.000007242198,0.0009029589,0.003453373,0.001353593,0.573159],"study_design_scores_gemma":[0.04710945,0.0007374278,0.5460727,0.008109717,0.003546134,0.0002243418,0.005703024,0.374709,0.003467463,0.008218518,0.0009680474,0.001134234],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8597379,0.006405316,0.1300809,0.0008770974,0.0001810926,0.001969798,0.0002907728,0.00005128418,0.0004058741],"genre_scores_gemma":[0.9768735,0.0008758394,0.02182043,0.0001766011,0.00006597715,0.00005396749,0.0001051764,0.00001954201,0.000009017062],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5720248,"threshold_uncertainty_score":0.3578854,"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."}}