{"id":"W2108093484","doi":"10.1038/srep14324","title":"Leveraging existing data sets to generate new insights into Alzheimer’s disease biology in specific patient subsets","year":2015,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; University of California, San Diego; BioClinica; F. Hoffmann-La Roche; Medpace; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Synarc; National Institute on Aging; Alzheimer's Association; Foundation for the National Institutes of Health","keywords":"Alzheimer's disease; Data science; Computational biology; Computer science; Disease; Bioinformatics; Biology; Medicine; Pathology","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":[0.0008231865,0.0001987958,0.0001814329,0.0001394454,0.0001445359,0.0001883199,0.0003872748,0.00009770835,0.00001046902],"category_scores_gemma":[0.0001240393,0.0001835727,0.00004105165,0.0002999499,0.00009545682,0.00001961159,0.0008333888,0.0001000416,0.0000348388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003611922,"about_ca_system_score_gemma":0.0004744513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009443625,"about_ca_topic_score_gemma":0.000144495,"domain_scores_codex":[0.9977324,0.0000566573,0.0006188914,0.000977205,0.0002158773,0.0003989647],"domain_scores_gemma":[0.9973273,0.000006078282,0.0002177874,0.001744231,0.0001153202,0.0005893372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002042568,0.0002276945,0.01360725,0.00003078267,0.0001124436,0.0008045429,0.006650534,0.0104172,0.1167531,0.0002096882,0.6775675,0.173415],"study_design_scores_gemma":[0.0003875954,0.00008848392,0.0007251966,0.0000492194,0.00002254167,0.0000618054,0.0002719926,0.004524603,0.01151171,0.01066906,0.9711057,0.0005821061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835668,0.006199244,0.003324031,0.0002263926,0.00539511,0.0003975249,0.000008307607,0.00001824853,0.0008644052],"genre_scores_gemma":[0.9923835,0.00002470285,0.005215381,0.0002477456,0.0002749684,0.00001021239,0.001573068,0.00002023207,0.000250221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2935382,"threshold_uncertainty_score":0.7485877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09063971210880892,"score_gpt":0.3038540156556223,"score_spread":0.2132143035468134,"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."}}