Assessing large-scale genomic language models in predicting personal gene expression: promises and limitations

Li, S.; Luo, R.; Huang, Y.

doi:10.1101/2025.07.09.664024

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bioRxiv · 10.1101/2025.07.09.664024

Assessing large-scale genomic language models in predicting personal gene expression: promises and limitations

Li, S.·Luo, R.·Huang, Y.

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Abstract

Large-scale genomic language models (gLMs) hold promise for modeling gene regulation, yet their ability to predict personal gene expression remains largely unexplored. We developed a framework, gLM2X-Tower, to benchmark gLMs and sequence-to-function (S2F) models on this task with paired personal genome-transcriptome data. With individual-level training, we found that similar to S2F models (e.g., AlphaGenome), gLMs (e.g., Evo2) remain incapable of predicting the inter-person variability on held-out genes. However, such training improves prediction for seen genes in new individuals, particularly by gLMs, highlighting the potential applications in few-shot settings like for rare variants

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Li, S., Luo, R., Huang, Y.. 2025-07-14. Assessing large-scale genomic language models in predicting personal gene expression: promises and limitations. https://doi.org/10.1101/2025.07.09.664024

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