Abstract Predicting gene expression from genomic sequences is a central goal in computational genomics. Recent advances have demonstrated that deep learning models trained on large-scale epigenomic datasets hold significant promise for this task. However, their success heavily depends on how they are applied: most models are trained exclusively on a reference genome, limiting their ability to capture individual-specific genetic variation. Consequently, while these models perform well on reference genomes, they often struggle when applied to personal genomic data. This review discusses recent efforts to overcome these limitations and explores methods aimed at improving the prediction of personalized gene expression. In particular, we compare the performance of deep learning models with traditional expression quantitative trait loci-based linear approaches, examining novel fine-tuning strategies, and highlighting the emergence of genomic language models. Across multiple studies, we find that deep learning models still face significant challenges in outperforming linear models for cross-individual gene expression prediction. Despite ongoing advances in model architecture and training methodology, accurately and robustly predicting personalized gene expression remains an open challenge in the field.
Dubey et al. (2026) studied this question.