An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions

The advent of multi-omics technologies has revolutionized biomedical research, enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic layers [Wang et al., 2021a]. This integrative paradigm has yielded unprecedented insights into the molecular architecture of complex diseases, particularly neurodegenerative disorders such as Alzheimer's disease. However, multi-omics datasets are often characterized by high-dimensional variables and limited sample sizes--a configuration known as high-dimension low-sample size (HDLSS). Under such constraints, conventional statistical methods suffer from reduced power and unrealistic assumptions [Fan and Lv, 2008], while deep learning models may exhibit overfitting and lack interpretability [LeCun et al., 2015]. Recent advances in dementia biomarker discovery have embraced multi-omics integration. For example, Iturria-Medina [2018] fused neuroimaging and omics data to identify disease-relevant signatures. Zhang [2020] employed transcriptomic-proteomic fusion to uncover molecular markers, and Lee [2022] demonstrated the discriminative utility of metabolomic features in Alzheimer's pathology. These efforts build upon foundational work in integrative omics [Hasin, 2017, Karczewski and Snyder, 2018], yet challenges persist in elucidating latent gene networks and selecting statistically robust features amidst inter-feature dependencies.