{"id":"W3135082407","doi":"10.1101/2021.02.23.21252283","title":"Integrated Transcriptomic and Neuroimaging Brain Model Decodes Biological Mechanisms in Aging and Alzheimer’s Disease","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"National Institute on Aging; Fonds de Recherche du Québec - Santé; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Health Canada; Health Resources and Services Administration; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Weston Brain Institute; McGill University; F. Hoffmann-La Roche; Pfizer; BioClinica; Biogen; Fondation Brain Canada; Novartis Pharmaceuticals Corporation; Canada First Research Excellence Fund; Bristol-Myers Squibb; U.S. Department of Defense; Eli Lilly and Company; Brain Research Foundation; Meso Scale Diagnostics; Alzheimer's Association; National Institutes of Health; U.S. Department of Health and Human Services","keywords":"Neuroimaging; Neuroscience; Disease; Brain aging; Cognitive decline; Psychology; Transcriptome; Dementia; Cognition; Biology; Medicine; Gene; Pathology; Gene expression; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002708792,0.0004027902,0.0003803266,0.000415574,0.0001802685,0.0006104509,0.0004750913,0.0006431616,0.001761574],"category_scores_gemma":[0.0007454341,0.0002350134,0.0007737424,0.0003009404,0.0004230903,0.0006095588,0.000385786,0.000501995,0.0003488411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007108678,"about_ca_system_score_gemma":0.0007007088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006936473,"about_ca_topic_score_gemma":0.005634607,"domain_scores_codex":[0.9999074,0.00002719947,0.000002982363,0.000035358,0.00001421898,0.0000129397],"domain_scores_gemma":[0.9998475,0.0000669057,0.00002486471,0.00002029567,0.00002124265,0.00001913764],"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":[0.00008950232,0.00004032185,0.00557245,0.00006964706,0.0001062719,0.0002605268,0.0000941457,0.8921685,0.01500905,0.06955741,0.001984798,0.01504739],"study_design_scores_gemma":[0.000006914858,0.00001304892,0.001486344,0.000004669555,0.00002413884,0.00006532253,0.00001450263,0.9463114,0.0008319581,0.05013021,0.001101967,0.000009504104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1990295,0.0006215977,0.7880813,0.001991197,0.00009204012,0.00003319064,0.002587127,0.001043348,0.006520669],"genre_scores_gemma":[0.9249306,0.0006216094,0.06680737,0.00027133,0.00008664407,0.0001132445,0.001413834,0.0001770339,0.005578273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006936473,"threshold_uncertainty_score":0.01379216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02812844830999285,"score_gpt":0.2582054988329276,"score_spread":0.2300770505229348,"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."}}