DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification
The introduction of Deep GCN shows encouraging performance in terms of supervised multi-omics feature learning, offering promising insights for precision medicine in cancer research. DeepMoIC can potentially be an important tool in the field of cancer subtype classification because of its capacity to handle complex multi-omics data and produce reliable classification findings.
Authors
- Shunxin Xiao (ORCID: https://orcid.org/0000-0003-1187-8719)
- Genggeng Liu (ORCID: https://orcid.org/0000-0002-3099-4371)
- Wenjie Wu (ORCID: https://orcid.org/0000-0002-0907-0561)
- Junnan Wu (ORCID: https://orcid.org/0000-0002-0624-2057)
- Zhaoliang Chen (ORCID: https://orcid.org/0000-0002-7832-908X)
- Shiping Wang (ORCID: https://orcid.org/0000-0002-8743-3129)
Institutions
- Fujian Medical University (CN)
- Hong Kong Baptist University (HK)
- Fujian Provincial Hospital (CN)
- Xiamen University of Technology (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- BMC Genomics
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12864-024-11112-5
- Citations
- 27
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 6.31
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Fujian Province