{"id":"W4281788067","doi":"10.1016/j.compbiomed.2022.105705","title":"Low-rank sparse feature selection with incomplete labels for Alzheimer's disease progression prediction","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Bristol-Myers Squibb Canada; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; Ministry of Science and Technology of the People's Republic of China; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; University of Southern California; Biogen; Bristol-Myers Squibb; Northern California Institute for Research and Education; Alzheimer's Drug Discovery Foundation; National Key Research and Development Program of China; AbbVie; National Institute on Aging; Sichuan Province Science and Technology Support Program; Alzheimer's Association","keywords":"Neuroimaging; Feature selection; Outlier; Computer science; Artificial intelligence; Pattern recognition (psychology); Missing data; Robustness (evolution); Matrix completion; Cognition; Matrix decomposition; Machine learning; Psychology; 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":[0.003186495,0.001320673,0.003122403,0.001235084,0.0006966438,0.001107125,0.002006483,0.001746896,0.001414087],"category_scores_gemma":[0.00844915,0.0006601051,0.001515333,0.001743642,0.0006678995,0.001298544,0.001135645,0.002400257,0.0009341813],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005595763,"about_ca_system_score_gemma":0.001830685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009716071,"about_ca_topic_score_gemma":0.009823703,"domain_scores_codex":[0.9985873,0.0005598412,0.00009768493,0.0002980997,0.0002803188,0.0001768012],"domain_scores_gemma":[0.9953352,0.003168853,0.0002764424,0.0003932924,0.000688214,0.00013802],"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":[0.00112754,0.0009955988,0.006819319,0.0004073266,0.0004077786,0.0002757094,0.0001468754,0.2762073,0.0073971,0.003525047,0.02277315,0.6799173],"study_design_scores_gemma":[0.0000349205,0.00007599263,0.0006761487,0.00001306342,0.00003585299,0.00003714295,0.00001589222,0.9954894,0.000657598,0.002546033,0.000406733,0.00001114391],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05653517,0.00262527,0.936098,0.0008607468,0.0001602658,0.0001170716,0.001161097,0.001869954,0.0005725251],"genre_scores_gemma":[0.7366124,0.001375332,0.2482122,0.000597365,0.0006190041,0.000409876,0.007414479,0.0001770832,0.004582225],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009716071,"threshold_uncertainty_score":0.019319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0218444456761385,"score_gpt":0.3448141855850518,"score_spread":0.3229697399089133,"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."}}