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bioRxiv · 10.1101/2025.05.14.654052

eSPred: Explainable scRNA-seq Prediction via Customized Foundation Models and Pathway-Aware Fine-tuning

Sun, L.·Yang, Q.·Zhang, J.·Guo, W.·Lin, L.
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Abstract

Single-cell RNA sequencing (scRNA-seq) has been widely used for studying cellular heterogeneity, but its use for subject-level prediction and clinical applications is still limited. We introduce eSPred, a customized foundation model designed for predictive analysis of scRNA-seq. It integrates cell-type information through a grouping strategy during pre-training and leverages pathway information to guide network flow during fine-tuning. Across multiple datasets, eSPred improves prediction accuracy and highlights pathways linked to disease mechanisms. These results suggest that eSPred can help bridge the gap between single-cell data and subject-level clinical insights, supporting more precise diagnosis and better-informed treatment decisions.

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Sun, L., Yang, Q., Zhang, J., Guo, W., Lin, L.. 2025-05-18. eSPred: Explainable scRNA-seq Prediction via Customized Foundation Models and Pathway-Aware Fine-tuning. https://doi.org/10.1101/2025.05.14.654052

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