ArticleNPJ digital medicine2025
A novel sequence-based transformer model architecture for integrating multi-omics data in preterm birth risk prediction.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Toward generalizable prediction of cancer signal using a cell-free DNA language model.Cell reports. Medicine · 2026Article
- Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability.Obstetrics & gynecology science · 2026Review
- A machine learning-based risk prediction model for early preterm birth: development and prospective validation.Frontiers in medicine · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Transformer-Based Multiomics Study Identifies Important Role of Glycine, Serine, and Threonine Metabolism Pathway in Rheumatoid Arthritis Complicated by Anemia.Computational and structural biotechnology journal · 2026Article
- Bridging ancient wisdom and modern technology: an AI and multi-omics framework for three causes tailored treatment in personalized medicine.Frontiers in molecular biosciences · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.