{"id":"W4403322286","doi":"10.48550/arxiv.2410.03937","title":"Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; University of Southern California; Biogen; BioClinica; European Commission; Meso Scale Diagnostics; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Association; Canadian Institutes of Health Research; National Science Foundation","keywords":"Cluster analysis; Similarity (geometry); Graph; Computer science; Artificial intelligence; Theoretical computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001344434,0.0002547321,0.0001841035,0.0002558353,0.00032749,0.0001370689,0.0002544904,0.0001677802,0.00006283218],"category_scores_gemma":[0.0001084222,0.0002888452,0.0001375649,0.0003875087,0.0001922613,0.0001045648,0.001173334,0.0009477202,0.00007107457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006744281,"about_ca_system_score_gemma":0.00005179124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000036704,"about_ca_topic_score_gemma":0.00003395945,"domain_scores_codex":[0.9981955,0.000221059,0.0001430457,0.001121252,0.00009197831,0.0002271785],"domain_scores_gemma":[0.9991338,0.0001172896,0.0001357685,0.000344468,0.00003315875,0.0002355538],"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":[0.003981105,0.00187148,0.08542196,0.005002784,0.0007545123,0.009935855,0.003885429,0.3958043,0.272691,0.1566394,0.001231024,0.06278106],"study_design_scores_gemma":[0.0003727818,0.00004090613,0.009459364,0.0001788814,0.0003918765,0.00001539978,0.0001362132,0.9380482,0.003219661,0.04597282,0.001501695,0.0006621288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9789376,0.0002687449,0.01676785,0.0003673638,0.0008621043,0.0003388911,0.00002088377,0.0005629276,0.00187363],"genre_scores_gemma":[0.9978615,0.0005318077,0.00001577083,0.0001094683,0.00006530755,0.000001641358,0.000008225241,0.00003106658,0.001375204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5422439,"threshold_uncertainty_score":0.9999564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07698733107137837,"score_gpt":0.2135441803529836,"score_spread":0.1365568492816052,"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."}}