Nicolas Jäger Gallego

Self-Attention over Parallel Dense Embeddings for High-Dimensional Omic Data

Results suggest that PLAT provides an effective and interpretable framework for high-dimensional transcriptomic classification and functional enrichment analyses consistently highlighted biological processes and disease pathways associated with breast cancer, supporting the biological relevance of the learned latent representations.