{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003768458,0.001386181,0.001626922,0.007885767,0.0005637925,0.002250954,0.001174824,0.0007208724,0.001189363],"category_scores_gemma":[0.01374599,0.000558209,0.002161201,0.004752528,0.0004917741,0.001550973,0.002435152,0.001243562,0.000588863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007159123,"about_ca_system_score_gemma":0.001070023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002920209,"about_ca_topic_score_gemma":0.007214947,"domain_scores_codex":[0.9981913,0.0006088035,0.0002126104,0.0005437427,0.0003542042,0.00008929492],"domain_scores_gemma":[0.9939465,0.003625643,0.0005264491,0.001117181,0.0005872793,0.0001970897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00122374,0.001031933,0.4155079,0.00133479,0.005230088,0.001694892,0.0009616443,0.1133913,0.0635129,0.006224081,0.00932977,0.3805571],"study_design_scores_gemma":[0.000321664,0.0008235772,0.1943972,0.000346061,0.002444773,0.002810345,0.001217968,0.6410144,0.02817357,0.09392102,0.03422524,0.0003041286],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.416704,0.003252597,0.5413152,0.003157016,0.0001902103,0.0008248856,0.02679943,0.003413947,0.004342721],"genre_scores_gemma":[0.6287331,0.001763548,0.3410198,0.0006500172,0.0001831352,0.0008773334,0.02576827,0.0003902945,0.0006144546],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007885767,"threshold_uncertainty_score":0.01992977,"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."}}