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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Accès en ligne: | https://arxiv.org/abs/2504.02148 |
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| _version_ | 1866911420116893696 |
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| author | Zhang, Heming Xu, Tim Cao, Dekang Liang, Shunning Shergill, Guntaas Hadas, Nicholas Schimmelpfennig, Lars Kaster, Levi Huang, Di Li, Guangfu Goedegebuure, S. Peter DeNardo, David Ding, Li Fields, Ryan C. Miller, J Philip Eghtesady, Pirooz Cruchaga, Carlos Buchser, William Cooper, Jonathan Sardiello, Marco Dickson, Patricia Chen, Yixin Province, Michael Payne, Philip Li, Fuhai |
| author_facet | Zhang, Heming Xu, Tim Cao, Dekang Liang, Shunning Shergill, Guntaas Hadas, Nicholas Schimmelpfennig, Lars Kaster, Levi Huang, Di Li, Guangfu Goedegebuure, S. Peter DeNardo, David Ding, Li Fields, Ryan C. Miller, J Philip Eghtesady, Pirooz Cruchaga, Carlos Buchser, William Cooper, Jonathan Sardiello, Marco Dickson, Patricia Chen, Yixin Province, Michael Payne, Philip Li, Fuhai |
| contents | With the rapid growth of large-scale single-cell omic datasets, omic foundation models (FMs) have emerged as powerful tools for advancing research in life sciences and precision medicine. However, most existing omic FMs rely primarily on numerical transcriptomic data by sorting genes as sequences, while lacking explicit integration of biomedical prior knowledge and signaling interactions that are critical for scientific discovery. Here, we introduce the Text-Omic Signaling Graph (TOSG), a novel data structure that unifies human-interpretable biomedical textual knowledge, quantitative omic data, and signaling network information. Using this framework, we construct OmniCellTOSG, a large-scale resource comprising approximately half million meta-cell TOSGs derived from around 80 million single-cell and single-nucleus RNA-seq profiles across organs and diseases. We further develop CellTOSG-FM, a multimodal graph language FM, to jointly analyze textual, omic and signaling network context. Across diverse downstream tasks, CellTOSG-FM outperforms existing omic FMs, and provides interpretable insights into disease-associated targets and signaling pathways. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02148 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling Zhang, Heming Xu, Tim Cao, Dekang Liang, Shunning Shergill, Guntaas Hadas, Nicholas Schimmelpfennig, Lars Kaster, Levi Huang, Di Li, Guangfu Goedegebuure, S. Peter DeNardo, David Ding, Li Fields, Ryan C. Miller, J Philip Eghtesady, Pirooz Cruchaga, Carlos Buchser, William Cooper, Jonathan Sardiello, Marco Dickson, Patricia Chen, Yixin Province, Michael Payne, Philip Li, Fuhai Artificial Intelligence Machine Learning With the rapid growth of large-scale single-cell omic datasets, omic foundation models (FMs) have emerged as powerful tools for advancing research in life sciences and precision medicine. However, most existing omic FMs rely primarily on numerical transcriptomic data by sorting genes as sequences, while lacking explicit integration of biomedical prior knowledge and signaling interactions that are critical for scientific discovery. Here, we introduce the Text-Omic Signaling Graph (TOSG), a novel data structure that unifies human-interpretable biomedical textual knowledge, quantitative omic data, and signaling network information. Using this framework, we construct OmniCellTOSG, a large-scale resource comprising approximately half million meta-cell TOSGs derived from around 80 million single-cell and single-nucleus RNA-seq profiles across organs and diseases. We further develop CellTOSG-FM, a multimodal graph language FM, to jointly analyze textual, omic and signaling network context. Across diverse downstream tasks, CellTOSG-FM outperforms existing omic FMs, and provides interpretable insights into disease-associated targets and signaling pathways. |
| title | OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2504.02148 |