{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001648349,0.0009163486,0.001063997,0.004605498,0.0007687628,0.001183904,0.001547577,0.001416775,0.0006519414],"category_scores_gemma":[0.004880878,0.000399584,0.001879841,0.002858817,0.0007913461,0.001479233,0.001253748,0.00109127,0.0006183971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001076514,"about_ca_system_score_gemma":0.001136057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01305188,"about_ca_topic_score_gemma":0.01612259,"domain_scores_codex":[0.9990166,0.0002917685,0.00008341866,0.0003441141,0.000166836,0.00009722458],"domain_scores_gemma":[0.9986295,0.0004540033,0.0002061361,0.000328486,0.0002919883,0.00008989673],"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":[0.0004874118,0.0004486939,0.02692767,0.0003268835,0.0007074905,0.0003327631,0.0007801003,0.3290174,0.02122979,0.01585855,0.009717852,0.5941654],"study_design_scores_gemma":[0.00002729959,0.00004484958,0.00261541,0.000009860043,0.00003329253,0.0001078062,0.00007275448,0.980136,0.002653953,0.01323971,0.001035183,0.00002389657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1782385,0.0007098202,0.8151665,0.0004736489,0.00005912238,0.0002842096,0.0009033069,0.002775762,0.001389046],"genre_scores_gemma":[0.5415568,0.0003352457,0.4524013,0.0001294963,0.00006809604,0.0001633829,0.00321996,0.0002724374,0.001853311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01305188,"threshold_uncertainty_score":0.0259518,"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."}}