OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Graph Language Foundation Modeling
Year: 2026
Type: preprint
Abstract: 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.
Source: Research Square
Authors: Fuhai Li, Heming Zhang, Tim Xu, Dekang Cao, Shunning Liang, Guntaas Shergill, Nicholas Hadas, Lars Schimmelpfennig, Levi Kaster, Di Huang, Guangfu Li, S. Goedegebuure, David DeNardo, Li Ding, Ryan Fields, J Philip Miller, Pirooz Eghtesady, Carlos Cruchaga, William Buchser, Jonathan Cooper, Marco Sardiello, Patricia Dickson, Yixin Chen, Michael Province, Philip Payne
Institutions: Washington University in St. Louis, University of Connecticut
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: AI models of multi-omic data integration for ming longevity core signaling pathways, Modeling and targeting tumor-immune signaling interactions in tumor microenvironment, Systems-Level Approach to Neuronopathic Lysosomal Storage Disorders, Combine Genomics and Symptoms Data Driven Models to Discover Synergistic Combinatory Therapies for Alzheimer's Disease, AI models of multi-omic data integration for ming longevity core signaling pathways
OpenAlex ID: W7129224998