<ul><li>A novel Graph Transformer Framework (GT-GRN) for Gene Regulatory Network (GRN) inference is introduced.</li><li>The GT-GRN model incorporates autoencoder embeddings, prior knowledge from GRN structures, and positional information of genes.</li><li>Using raw data, the autoencoder captures gene expression patterns to preserve biological signals.</li><li>Experimental results demonstrate that GT-GRN outperforms existing GRN inference methods in terms of accuracy.</li></ul>

A Novel Graph Transformer Framework for Gene Regulatory Network Inference

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