{"id":"W2072188503","doi":"10.1016/j.neuroimage.2013.06.033","title":"Locally linear embedding (LLE) for MRI based Alzheimer's disease classification","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":164,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute on Aging; National Institutes of Health; Genentech; IXICO; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, Los Angeles; Servier; Eisai; Northern California Institute for Research and Education; Pfizer; Biogen; BioClinica; Synarc; Bayer HealthCare; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Medpace; AstraZeneca; Bristol-Myers Squibb; Eli Lilly and Company; Novartis Pharmaceuticals Corporation; National Center for Research Resources; F. Hoffmann-La Roche; Amorfix Life Sciences; Alzheimer's Drug Discovery Foundation; University of California, San Diego; U.S. Department of Veterans Affairs","keywords":"Neuroimaging; Artificial intelligence; Linear discriminant analysis; Multivariate statistics; Pattern recognition (psychology); Logistic regression; Alzheimer's Disease Neuroimaging Initiative; Support vector machine; Medical diagnosis; Machine learning; Linear classifier; Computer science; Alzheimer's disease; Medicine; Disease; Psychology; Pathology; Neuroscience","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":[0.00006276532,0.0001632892,0.00016834,0.00008327181,0.0001327748,0.00003400085,0.0001343011,0.00003997432,0.00009604119],"category_scores_gemma":[0.0001345533,0.0001535945,0.0001219374,0.0001591006,0.00007927816,0.0001459043,0.00003089786,0.0001694228,0.0001106222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002128414,"about_ca_system_score_gemma":0.00007118103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000479727,"about_ca_topic_score_gemma":1.527268e-7,"domain_scores_codex":[0.9988791,0.00002032697,0.0002420579,0.0004432932,0.0001617034,0.0002535297],"domain_scores_gemma":[0.9987267,0.0001193085,0.00009236039,0.0006276525,0.0001836836,0.0002503201],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009267796,0.00239368,0.01718884,0.0005989138,0.00009873424,0.0001230442,0.00008073459,0.001498361,0.5823364,0.01225286,0.2454708,0.1370309],"study_design_scores_gemma":[0.0013093,0.0002247136,0.05912299,0.00007261486,0.000229174,0.00001208807,0.00001238205,0.8049894,0.009787961,0.001828991,0.1221041,0.0003062851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01462237,0.0001158734,0.9457029,0.03296873,0.00009628966,0.003565822,0.00005688384,0.0009907946,0.0018803],"genre_scores_gemma":[0.7885182,0.00003483801,0.2027029,0.006535386,0.0002304808,0.001231123,0.0001615319,0.00009779651,0.0004877535],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.803491,"threshold_uncertainty_score":0.6263402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.109749089953746,"score_gpt":0.3852019442355012,"score_spread":0.2754528542817552,"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."}}