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✅ Cross-tissue Graph Attention Networks for Semi-supervised Gene Expression Prediction

tech

High-throughput biotechnologies have significantly advanced precision medicine by enabling the exploitation of global gene expression patterns to enhance our understanding of disease etiology, progression, and treatment options. However, the tissue-specific nature of gene expression presents a challenge, particularly for less accessible tissues such as the brain, underscoring the need for computational methods to accurately impute gene expression in these critical but hard-to-reach tissues. While several attempts to impute gene expression in tissue-specific contexts have shown promising results, their reliance on regression analysis faces limitations due to the inability to capture complex, nonlinear relationships in gene expression patterns. In contrast, modern machine learning techniques, particularly graph neural networks, have demonstrated superior performance by efficiently modeling the intricate interactions ...

BioRxiv     Nov 18, 2024