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

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.

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Publication Details

Journal
npj Digital Medicine
Published
2025-08-20
DOI
https://doi.org/10.1038/s41746-025-01942-2
Citations
3
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
2.81

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article

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

Shida Zhu, Chenchen Guan, Siwei Deng, Wenzhi Yang et al.
3 citations
npj Digital Medicine
Cancer-related molecular mechanisms research
2.81
article

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

Shida Zhu, Chenchen Guan, Siwei Deng, Wenzhi Yang, Jinying Yang, Jian‐Guo Zhang, Hefeng Huang, Hai‐Xi Sun, Yongcheng Jin, Yibing Zhu, Hui Jiang, Xiao Zhang, Xinrui Wang, Si Zhou, Danling Cheng, Lijian Zhao
article en
3 citations

Abstract

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.

npj Digital MedicineVol. 8(1)
BGI Group (China) (CN), Hebei Medical University (CN), Fujian Medical University (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Beijing Graphene Institute (CN), Shenzhen Maternity and Child Healthcare Hospital (CN), Ministry of Education (TH), Women's Hospital, School of Medicine, Zhejiang University (CN), Shijiazhuang Yiling Pharmaceutical (China) (CN), Obstetrics and Gynecology Hospital of Fudan University (CN), Shenzhen Technology University (CN), Hebei Science and Technology Department (CN), University of Chinese Academy of Sciences (CN), BGI Genomics, BGI Research (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 9%
Cancer-related molecular mechanisms research
2.81
DOIOpen Access PDFOpenAlex
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