{"id":"W2899606321","doi":"10.1371/journal.pone.0206607","title":"Data-driven, voxel-based analysis of brain PET images: Application of PCA and LASSO methods to visualize and quantify patterns of neurodegeneration","year":2018,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Lasso (programming language); Voxel; Principal component analysis; Pattern recognition (psychology); Artificial intelligence; Functional principal component analysis; Positron emission tomography; Computer science; Covariate; Mathematics; Nuclear medicine; Medicine; Machine learning","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":[0.00197853,0.0009885776,0.0009396886,0.0008319083,0.00024703,0.001064742,0.0006647792,0.0007456758,0.0004897376],"category_scores_gemma":[0.003591308,0.0004274207,0.001176493,0.0008389978,0.0006054837,0.0005559142,0.000640281,0.001246168,0.0002137868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003275106,"about_ca_system_score_gemma":0.0007968218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001843766,"about_ca_topic_score_gemma":0.001790325,"domain_scores_codex":[0.9992931,0.00029332,0.00004724568,0.0001629383,0.0001607152,0.0000426071],"domain_scores_gemma":[0.998567,0.0007853235,0.0002702729,0.0001262919,0.0002083162,0.00004286043],"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":[0.0001833455,0.0001420378,0.00543609,0.0002705684,0.0003970663,0.0002316611,0.000213385,0.7655008,0.04025724,0.006921404,0.002529969,0.1779164],"study_design_scores_gemma":[0.000003696022,0.00002253627,0.001288281,0.000005358743,0.000009107083,0.00003779839,0.00001080695,0.993658,0.002128549,0.002357901,0.0004611297,0.0000167562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02326256,0.0002237745,0.9753237,0.0001791959,0.00002268193,0.00003338718,0.0001887682,0.0005361714,0.0002296265],"genre_scores_gemma":[0.479124,0.0006534067,0.5166326,0.0001852919,0.00009006716,0.0003734245,0.001331683,0.0003173967,0.001292217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00197853,"threshold_uncertainty_score":0.0104636,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1882450430924438,"score_gpt":0.4498888425045691,"score_spread":0.2616437994121252,"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."}}