Back to Search
Start Over
Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning.
- Source :
-
BMC bioinformatics [BMC Bioinformatics] 2024 Mar 27; Vol. 25 (1), pp. 132. Date of Electronic Publication: 2024 Mar 27. - Publication Year :
- 2024
-
Abstract
- Background: Classifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes.<br />Results: In this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data.<br />Conclusions: The experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.<br /> (© 2024. The Author(s).)
Details
- Language :
- English
- ISSN :
- 1471-2105
- Volume :
- 25
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 38539064
- Full Text :
- https://doi.org/10.1186/s12859-024-05749-y