{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001068483,0.0006487438,0.0009409824,0.00105091,0.000386223,0.0007939296,0.0008310957,0.0009254087,0.001314366],"category_scores_gemma":[0.002610565,0.0002973784,0.001006496,0.0007907942,0.0003429286,0.0008035084,0.0008958183,0.001160406,0.0008145614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003403708,"about_ca_system_score_gemma":0.0007216316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003971385,"about_ca_topic_score_gemma":0.005421672,"domain_scores_codex":[0.9994845,0.0002033053,0.00003994075,0.0001132577,0.0001067,0.00005215026],"domain_scores_gemma":[0.9991859,0.0004145254,0.00008885636,0.0001209433,0.0001505652,0.00003919119],"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":[0.0004370055,0.0002805284,0.0029614,0.0002336016,0.0002288484,0.0001779751,0.0001533526,0.1039101,0.02198548,0.006184824,0.01023478,0.8532121],"study_design_scores_gemma":[0.00001155646,0.0000893422,0.001005106,0.00001934816,0.00003817274,0.0001382789,0.00004313831,0.9869431,0.004366873,0.005925168,0.001398499,0.00002134853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04086119,0.001963648,0.9535089,0.0005599845,0.0001004551,0.00006118073,0.0004221985,0.001685899,0.0008365844],"genre_scores_gemma":[0.5866701,0.001211935,0.4034585,0.0002795794,0.0001894227,0.0001932284,0.001355642,0.0002984095,0.006343228],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003971385,"threshold_uncertainty_score":0.007896543,"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."}}