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.DM2025
Perfect phylogenies via the Minimum Uncovering Branching problem: efficiently solvable cases
Narmina Baghirova, Esther Galby, Martin Milanič
In this paper, we present new efficiently solvable cases of the Minimum Uncovering Branching problem, an optimization problem with applications in cancer genomics introduced by Huj…
cs.LG2025
Explainable Graph-theoretical Machine Learning: with Application to Alzheimer's Disease Prediction
Narmina Baghirova, Duy-Thanh Vũ, Duy-Cat Can +6
Alzheimer's disease (AD) affects 50 million people worldwide and is projected to overwhelm 152 million by 2050. AD is characterized by cognitive decline due partly to disruptions i…