{"id":"W2901244611","doi":"10.3389/fnagi.2018.00365","title":"Multiplex Networks for Early Diagnosis of Alzheimer's Disease","year":2018,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Synarc; Meso Scale Diagnostics; University of California, San Diego; BioClinica; Bristol-Myers Squibb; Eli Lilly and Company; Medpace; Biogen; Foundation for the National Institutes of Health","keywords":"Multiplex; Disease; Alzheimer's disease; Neuroscience; Medicine; Psychology; Bioinformatics; Biology; Internal medicine","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":[0.0009427893,0.0007956458,0.0004313502,0.001764421,0.0002542146,0.0006863559,0.0003680347,0.0004763011,0.001507801],"category_scores_gemma":[0.00274019,0.0002225311,0.0003175745,0.0005734707,0.0002672858,0.0008216497,0.0006333001,0.000327663,0.0003125795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000575067,"about_ca_system_score_gemma":0.0002663181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003416539,"about_ca_topic_score_gemma":0.004402608,"domain_scores_codex":[0.9997545,0.0001041078,0.00001175376,0.0000637477,0.00004518437,0.00002069435],"domain_scores_gemma":[0.9993211,0.0002865368,0.0001786666,0.00006917223,0.00007491144,0.00006963661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002751112,0.0004373973,0.197228,0.0002847572,0.0008909605,0.0008790459,0.000442441,0.3249179,0.03439871,0.01717523,0.005511565,0.4150829],"study_design_scores_gemma":[0.00002502177,0.0002192565,0.05580042,0.00003959345,0.0001542332,0.0005792891,0.0000714288,0.9130179,0.004333436,0.02375563,0.001963445,0.00004042244],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6729975,0.002691805,0.3153466,0.0008820854,0.00007691253,0.0001517319,0.001928207,0.0008927555,0.005032326],"genre_scores_gemma":[0.9631886,0.0005743168,0.03452436,0.00003160654,0.00005553362,0.00006125727,0.0004199578,0.00002100316,0.001123241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003416539,"threshold_uncertainty_score":0.00679332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04590309089801075,"score_gpt":0.2814147756957315,"score_spread":0.2355116847977207,"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."}}