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A multimodal cell-free RNA language model for liquid biopsy applications.

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Název: A multimodal cell-free RNA language model for liquid biopsy applications.
Autoři: Karimzadeh, Mehran, Sababi, Aiden M., Momen-Roknabadi, Amir, Chen, Nae-Chyun, Cavazos, Taylor B., Sekhon, Sukh, Wang, Jieyang, Hanna, Rose, Huang, Alice, Nguyen, Dang, Chen, Selina, Lam, Ti, Chau, Kimberly H., Hartwig, Anna, Fish, Lisa, Li, Helen, Behsaz, Babak, Hormozdiari, Fereydoun, Alipanahi, Babak, Goodarzi, Hani
Zdroj: Nature Machine Intelligence; Dec2025, Vol. 7 Issue 12, p1927-1938, 12p
Abstrakt: Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multimodal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pretrained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for the preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics. Exai-1, a cell-free RNA foundation model that integrates sequence, structure and expression features, advances liquid biopsy diagnostics by denoising noisy data, augmenting limited datasets and improving the generalizability of cancer detection models. [ABSTRACT FROM AUTHOR]
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Databáze: Biomedical Index
Popis
Abstrakt:Cell-free RNA (cfRNA) profiling has emerged as a powerful tool for non-invasive disease detection, but its application is limited by data sparsity and complexity, especially in settings with constrained sample availability. We introduce Exai-1, a multimodal, transformer-based generative foundation model that integrates RNA sequence embeddings with cfRNA abundance data to capture biologically meaningful representations of circulating RNAs. By leveraging both sequence and expression modalities, Exai-1 captures a biologically meaningful latent structure of cfRNA profiles. Pretrained on over 306 billion tokens from 8,339 samples, Exai-1 enhances signal fidelity, reduces technical noise and improves disease detection by generating synthetic cfRNA profiles. We show that self-attention and variational inference are particularly important for the preservation of biological signals and contextual relationships. Additionally, Exai-1 facilitates cross-biofluid translation and assay compatibility through disentangling biological signals from confounders. By uniting sequence-informed embeddings with cfRNA expression patterns, Exai-1 establishes a transfer learning foundation for liquid biopsy, offering a scalable and adaptable framework for next-generation cfRNA-based diagnostics. Exai-1, a cell-free RNA foundation model that integrates sequence, structure and expression features, advances liquid biopsy diagnostics by denoising noisy data, augmenting limited datasets and improving the generalizability of cancer detection models. [ABSTRACT FROM AUTHOR]
ISSN:25225839
DOI:10.1038/s42256-025-01148-x