MoAGL-SA: a multi-omics adaptive integration method with graph learning and self attention for cancer subtype classification
Experimental results show that MoAGL-SA outperforms several popular algorithms on datasets for breast invasive carcinoma, kidney renal papillary cell carcinoma, and kidney renal clear cell carcinoma. Additionally, MoAGL-SA successfully identifies key biomarkers for breast invasive carcinoma.
Authors
- Shuguang Ge (ORCID: https://orcid.org/0000-0002-3583-2742)
- Ping Gong (ORCID: https://orcid.org/0009-0001-4680-2530)
- Yanan Li (ORCID: https://orcid.org/0000-0002-1868-3626)
- Zunmin Zhu (ORCID: https://orcid.org/0000-0002-6635-7454)
- Longzhen Zhang (ORCID: https://orcid.org/0000-0002-5786-1560)
- Lei Cheng (ORCID: https://orcid.org/0000-0002-4204-8462)
- Qian Huang (ORCID: https://orcid.org/0000-0002-1044-3361)
Institutions
- Xuzhou Medical College (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2024-11-23
- DOI
- https://doi.org/10.1186/s12859-024-05989-y
- Citations
- 8
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Xuzhou Medical University