A novel graph-based patient embedding method for the diagnosis of Alzheimer’s disease from microRNA expression data

Abstract As key players of gene expression regulation, microRNAs (miRNAs) are involved in the onset and progression of many diseases. Because of their presence in organic fluid, such as blood or urine, they are optimal candidates as disease biomarkers that can be easily detected in liquid biopsy, which is less invasive and expensive than traditional techniques. In this paper, we propose a novel patient embedding method, called GraphiRNA, to support the diagnosis of Alzheimer’s disease from miRNA expression values. GraphiRNA is capable of capturing information about the relationships among miRNAs, estimated through the correlation of their expression values over a set of patients, and also integrate the information about their RNA sequences. Notably, GraphiRNA is able to properly model negative correlations by introducing synthetic nodes into the network, to avoid discarding the sign of correlations (and the associated information), as done by existing approaches. Our experimental evaluation, performed on real data from three studies on Alzheimer’s disease, showed superior classification performances of GraphiRNA, especially in patients affected by Mild Cognitive Impairment (MCI), which outlines the potential of GraphiRNA of being adopted as a diagnostic tool for the early intervention. Finally, our ablation study on the approach adopted to handle negative correlations showed an improvement reaching 30.9% in terms of F1-score over an approach that discards the sign of correlations, confirming our initial intuition about the importance of properly considering such information.