3 papers
q-bio.NC2026
Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts
Christelle Schneuwly Diaz, Narmina Baghirova, Duy-Thanh Vu +4
Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left un…
cs.LG2025
Explainable Graph-theoretical Machine Learning with Application to Alzheimer's Disease Prediction
Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can +6
Dementia affects over 55 million people worldwide, projected to reach 139 million by 2050, with Alzheimer's disease (AD) accounting for 60-70% of cases. AD is associated with disru…
cs.LG2025
OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values
Christelle Schneuwly Diaz, Duy-Thanh Vu, Julien Bodelet +7
Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and behavioral faculties. Traditiona…