Document Type
Article
Publication Date
2024
DOI
10.1093/bioinformatics/btae257
Publication Title
Bioinformatics
Volume
40
Issue
Issue Supplement_1
Pages
i511-i520
Abstract
Motivation
Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited.
Results
Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes.
Availability and implementation
DISHyper is freely available for download at https://github.com/genemine/DISHyper.
Rights
© The Authors 2024.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Data Availability
Article states: "All datasets utilized in this study are publicly available, and the data underlying this article are available at https://github.com/genemine/DISHyper."
Original Publication Citation
Deng, C., Li, H.-D., Zhang, L.-S., Liu, Y., Li, Y., & Wang, J. (2024). Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks. Bioinformatics, 40(Supplement_1), i511-i520. https://doi.org/10.1093/bioinformatics/btae257
Repository Citation
Deng, C., Li, H.-D., Zhang, L.-S., Liu, Y., Li, Y., & Wang, J. (2024). Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks. Bioinformatics, 40(Supplement_1), i511-i520. https://doi.org/10.1093/bioinformatics/btae257
Supplementary Material
Included in
Artificial Intelligence and Robotics Commons, Cancer Biology Commons, Computational Biology Commons