CDBridge: A Cross-omics Post-training Bridge Strategy for Context-aware Biological Modeling

Abstract

Linking genomic DNA to quantitative, context-specific expression remains a central challenge in computational biology. Current foundation models capture either tissue context or sequence features, but not both. Cross-omics systems, in turn, often overlook critical mechanisms such as alternative splicing and isoform reuse. We present CDBridge, a post-training strategy that unifies pretrained DNA and protein models into a context-aware framework without full retraining. CDBridge operates in two stages: (a) Seq-context learning, where a splicing-inspired token merge compresses long genomic regions into isoform-aware representations, and (b) Env-context learning, where a conditional decoder injects tissue embeddings to model expression under diverse biological contexts. To benchmark this setting, we introduce GTEx-Benchmark, derived from GTEx and Ensembl, which requires models to capture long-range exon dependencies, resolve isoform reuse, and predict tissue-specific expression levels. Across qualitative and quantitative tasks, CDBridge consistently outperforms prior methods that ignore central dogma constraints or context dependence, offering a scalable and biologically faithful solution for DNA-to-expression modeling.