Interpretable identification of cancer genes across biological networks via transformer-powered graph representation learning
Chinese Academy of Sciences · University of Illinois Chicago · +4 more institutions
Indexed incrossrefpubmed
Abstract
No abstract available for this paper.
Citation impact
66
total citations
- FWCI
- 40.67
- Percentile
- 100%
- References
- 81
Citations per year
Authors
8- XSXiaorui SuCorresponding
Chinese Academy of Sciences, University of Illinois Chicago, Xinjiang Technical Institute of Physics & Chemistry, University of Chinese Academy of Sciences
- PHPengwei Hu
Chinese Academy of Sciences, Xinjiang Technical Institute of Physics & Chemistry, University of Chinese Academy of Sciences
- DLDongxu Li
Chinese Academy of Sciences, Xinjiang Technical Institute of Physics & Chemistry, University of Chinese Academy of Sciences
- BZBo-Wei Zhao
Chinese Academy of Sciences, Xinjiang Technical Institute of Physics & Chemistry, University of Chinese Academy of Sciences
- ZNZhaomeng Niu
Rutgers, The State University of New Jersey
Topics & keywords
Topics
Keywords
- Interpretability
- Biological network
- Gene regulatory network
- Computational biology
- Computer science
- Generalizability theory
- Graph
- Gene
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