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<p>Preterm birth (PTB) significantly contributes to maternal and perinatal mortality and lifelong morbidity. While large language models (LLM) offer considerable potential for disease risk prediction and early detection, their application to PTB prediction using multi-omics data remains limited. We developed a novel transformer-based architecture for integrating cell free (cfDNA) and cfRNA sequencing data for PTB risk prediction. In the test set, the cfDNA LLM model achieved an AUC of 0.822, and the cfRNA LLM model achieved 0.851. Integrating cfDNA and cfRNA data within the transformer-based framework outperformed both, reaching an AUC of 0.890, a significant improvement over single-modality models. Additionally, we explored cfRNA and cfDNA integration using RNA editing and achieved an AUC of 0.82. This underscores the potential of multi-omics data fusion, with transformer-based architectures providing a powerful framework for disease risk assessment, and demonstrates the potential of AI-driven multi-omics for broader applications in precision obstetrics and biomedicine.</p>

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A novel sequence-based transformer model architecture for integrating multi-omics data in preterm birth risk prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月20日

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  • Si Zhou,
  • Chenchen Guan,
  • Siwei Deng,
  • Yibing Zhu,
  • Wenzhi Yang,
  • Xiao Zhang,
  • Xinrui Wang,
  • Jinying Yang,
  • Shida Zhu,
  • Hui Jiang,
  • Jianguo Zhang,
  • Yongcheng Jin,
  • Danling Cheng,
  • Hai-Xi Sun,
  • Lijian Zhao,
  • Hefeng Huang

摘要

Preterm birth (PTB) significantly contributes to maternal and perinatal mortality and lifelong morbidity. While large language models (LLM) offer considerable potential for disease risk prediction and early detection, their application to PTB prediction using multi-omics data remains limited. We developed a novel transformer-based architecture for integrating cell free (cfDNA) and cfRNA sequencing data for PTB risk prediction. In the test set, the cfDNA LLM model achieved an AUC of 0.822, and the cfRNA LLM model achieved 0.851. Integrating cfDNA and cfRNA data within the transformer-based framework outperformed both, reaching an AUC of 0.890, a significant improvement over single-modality models. Additionally, we explored cfRNA and cfDNA integration using RNA editing and achieved an AUC of 0.82. This underscores the potential of multi-omics data fusion, with transformer-based architectures providing a powerful framework for disease risk assessment, and demonstrates the potential of AI-driven multi-omics for broader applications in precision obstetrics and biomedicine.