An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions
This work addresses the challenge of identifying robust biomarkers for neurodegenerative diseases like dementia, which could aid in diagnosis and treatment, though it appears incremental as it combines existing techniques into a new ensemble.
The researchers tackled biomarker discovery for dementia using multi-omics data under high-dimensional, low-sample-size conditions, proposing an ensemble framework that achieved superior predictive accuracy, feature selection precision, and biological relevance compared to state-of-the-art methods.
Biomarker discovery in neurodegenerative diseases requires robust, interpretable frameworks capable of integrating high-dimensional multi-omics data under low-sample conditions. We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR). This framework is benchmarked against state-of-the-art methods including DIABLO, MOCAT, AMOGEL, and MOMLIN. We evaluate performance using both simulated multi-omics data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Our method demonstrates superior predictive accuracy, feature selection precision, and biological relevance. Biomarker gene maps derived from both datasets are visualized and interpreted, offering insights into latent molecular mechanisms underlying dementia.