mosGraphGen: a novel tool to generate multi-omic signaling graphs to facilitate integrative and interpretable graph AI model development

Abstract

Multi-omic data, i.e., genomics, epigenomics, transcriptomics, proteomics, characterize cellular complex signaling systems from multi-level and multi-view and provide a holistic view of complex cellular signaling pathways. However, it remains challenging to integrate and interpret multi-omics data. Graph neural network (GNN) AI models have been widely used to analyze graph-structure datasets and are ideal for integrative multi-omics data analysis because they can naturally integrate and represent multi-omics data as a biologically meaningful multi-level signaling graph and interpret multi-omics data by node and edge ranking analysis for signaling flow/cascade inference. However, it is non-trivial for graph-AI model developers to pre-analyze multi-omics data and convert them into graph-structure data for individual samples, which can be directly fed into graph-AI models. To resolve this challenge, we developed mosGraphGen (multi-omics signaling graph generator), a novel computational tool that generates multi-omics signaling graphs of individual samples by mapping the multi-omics data onto a biologically meaningful multi-level background signaling network. With mosGraphGen, AI model developers can directly apply and evaluate their models using these mos-graphs. We evaluated the mosGraphGen using both multi-omics datasets of cancer and Alzheimers disease (AD) samples. The code of mosGraphGen is open-source and publicly available via GitHub: https://github.com/Multi-OmicGraphBuilder/mosGraphGen

mosGraphFlow: a novel integrative graph AI model mining disease targets from multi-omic data

“…This intersection resulted in 2144 gene entities. 20 with the advanced predictive functionalities of M3NetFlow 21 (see Figure 1). By combining the strengths of both models, the integrated approach offers a robust solution for multi-omics data analysis with generation of 𝒢 = (𝑉, 𝐸), where |𝑉| = 𝑛.…”

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI

“…AD is commonly defined using criteria such as the CERAD (Consortium to Establish a Registry for Alzheimer's Disease) score 9 , which evaluates the density of neurotic plaques to classify the severity of the disease. Many reports of omics data and analyses of AD have been published [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . However, the pathogenesis of AD remains unclear and there is a lack of effective prevention and curable treatment medications.…”

“…This approach enhances the inference process, allowing for a more nuanced understanding of the underlying biological processes 36 . Another noteworthy model is mosGraphGen (multi-omics signaling graph generator) 12 , which generates multi-omics signaling graphs for individual samples. This tool maps multi-omics data onto a biologically meaningful multi-level signaling network, enabling integrative and interpretable multi-omics data analysis using GNN models.…”