A guide to multi-omics data collection and integration for translational medicine

Ensemble-GNN: federated ensemble learning with graph neural networks for disease module discovery and classification

“…In further updates of the herein proposed Python package we will work on the issue (i) since transcriptomics is one of the most common omics types [13]. Thus, we plan to test several GNN architectures converging well on transcriptomics data, including the GNN approach from [14] which was successfully applied to gene expression data and interpreted in [15].…”

Opportunities and challenges of 5G network technology toward precision medicine

“…The synergy between multi‐omics and non‐omics data and the advent of 5G network technology has been deployed to improve the healthcare system. Athieniti and Spyrou 29 have comprehensively reviewed the incorporation of various omics technologies through computational tools, such as Multi‐Omics Factor Analysis (integrating datasets of genomics, epigenomics, transcriptomics, and proteomics) and more.…”

Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons

“…Multiomics analysis has been used to unravel the complex biological processes underlying neurodegenerative diseases. ,, Multiomics experiments must be paired with sophisticated bioinformatic tools to decipher the large-scale multidimensional data sets and integrate different omics data sets into the same biological processes, where genes control protein expression and proteins serve as enzymes for lipid and metabolic pathways. − Network-based multiomics integration perceives biological systems as interconnected units, where each omics layer contributes to uncovering the authentic connections within the networks . Enabled by these bioinformatics tools, integrating proteomics, lipidomics, and metabolomics analyses of iPSCs and neurons can provide us a holistic view of the neuron differentiation process and allow us to understand the alteration of molecular profiles under both healthy and disease states.…”