Fig. 1
From: Immune status assessment based on plasma proteomics with meta graph convolutional networks
The Overall workflow of the ProMetaGCN model, which consists of two steps: the prediction of immune-related proteins and the assessment of immune status. In Step 1, (a) The meta-learner module optimizes the model parameters, with training divided into m metagraphs; (b) The working mechanism of the GCN in immune-related protein prediction. The input to the Meta-GCN model includes feature matrices and adjacency matrices, which facilitate the classification of nodes based on the graph’s topological structure. In step 2, (c) The selection of multiple machine learning methods and downstream analyses (including GO/KEGG, correlation analysis, and validation with independent test datasets). (d) the age and gender distribution pyramid chart for the healthy sample dataset utilized in this study