Fig. 1: Schematic of our end-to-end integrative model GNNRAI. | npj Systems Biology and Applications

Fig. 1: Schematic of our end-to-end integrative model GNNRAI.

From: Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks

Fig. 1

Data from individual ‘omics modalities are processed in their respective GNN feature extractors to produce low-dimensional embeddings (z1 and z2), which are then aligned and integrated through a set transformer. Samples with incomplete multi-omics measures have their embeddings processed through separate MLP (multi-layer perceptron) classifiers to produce modality-specific predictions of the target. Some elements of the figure were rendered using functions from Matplotlib in Python, while the entire figure was created in PowerPoint.

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