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W. Cheng

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Open access Jul 2026

OmicFormer: a statistical priors-informed transformer for accurate and generalizable omics prediction of diseases and complex traits

OmicFormer, a Transformer-based architecture that embeds two complementary statistical priors, i.e., feature-label associations and feature-feature dependencies, directly into its representation learning, provides an interpretable and generalizable foundation for omics-based precision medicine.

H. Jiang, C. Yang, M. Qin et al. · 0 citations

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