GeneCompass: Deciphering Universal Gene Regulatory Mechanisms with Knowledge-Informed Cross-Species Foundation Model
Open accessPreprint — published as 10.1038/s41422-024-01034-y · Paper page
Abstract
Deciphering the universal gene regulatory mechanisms in diverse organisms holds great potential to advance our knowledge of fundamental life process and facilitate research on clinical applications. However, the traditional research paradigm primarily focuses on individual model organisms, resulting in limited collection and integration of complex features on various cell types across species. Recent breakthroughs in single-cell sequencing and advancements in deep learning techniques present an unprecedented opportunity to tackle this challenge. In this study, we developed GeneCompass, the first knowledge-informed, cross-species foundation model pre-trained on an extensive dataset of over 120 million single-cell transcriptomes from human and mouse. During pre-training, GeneCompass effectively integrates four types of biological prior knowledge to enhance the understanding of gene regulatory mechanisms in a self-supervised manner. Fine-tuning towards multiple downstream tasks, GeneCompass outperforms competing state-of-the-art models in multiple tasks on single species and unlocks new realms of cross-species biological investigation. Overall, GeneCompass marks a milestone in advancing knowledge of universal gene regulatory mechanisms and accelerating the discovery of key cell fate regulators and candidate targets for drug development.
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Yang et al. introduced GeneCompass, the first knowledge-based cross-species milestone foundational model, surpassing competitive state-of-the-art models in multiple tasks within a single species.
“In the realm of genetic data, Yang et al. [49] introduced GeneCompass, the first knowledge-based cross-species milestone foundational model, surpassing competitive state-of-the-art models in multiple tasks within a single species.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [49]
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Sinaga, U. M. (2009)
- Authors
- Yang, X., Liu, G., Feng, G., Bu, D., Wang, P., Jiang, J., Chen, S., Yang, Q., Zhang, Y., Man, Z., Liang, Z., Wang, Z., Li, Y., Li, Z., Liu, Y., Tian, Y., Li, A., Dong, J., Hu, Z., Fang, C., Miao, H., Cui, L., Deng, Z., Jiang, H., Cui, W., Zhang, J., Yang, Z., Li, H., He, X., Zhong, L., Zhou, J., Wang, Z., Long, Q., Xu, P., The X-Compass Consortium, Wang, H., Meng, Z., Wang, X., Wang, Y., Wang, Y., Zhang, S., Guo, J., Zhao, Y., Zhou, Y., Li, F., Liu, J., Chen, Y., Yang, G., & Li, X.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Zhao, Yi
- Venue
- Cell Research
- Publisher
- openRxiv
- Published
- 2023-09-28
- Volume
- 34
- Issue
- 12
- Pages
- 830-845
- ISSN
- 1001-0602 (ISSN-L)
- PMC
- PMC11615217
- OpenAlex
- W4403174218
- Cited by
- 148
- References
- 75
- Citations by year
- 2026: 63 · 2025: 63 · 2024: 20 · 2023: 1
- FWCI
- 19.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Cell Research (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (62202455, No.91954201, No.32341013, 31971289, 91954201, No.62202455, No.31971289, 32341019, 32341013)
- Chinese Academy of Sciences (YSBR‐076, CAS-WX2021SF-0101, YSBR-034)
- Institute of Zoology, Chinese Academy of Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gene regulatory mechanisms, single-cell transcriptomics, self-supervised learning, cell fate transitions, gonadal differentiation, human embryonic stem cells, drug target identification
- MeSH terms
- Animals, Cell Differentiation, Gene Expression Regulation, Humans, Species Specificity, Mice, Gene Regulatory Networks, Single-Cell Analysis, Transcriptome
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