AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data
Abstract Background Cancer classification has consistently been a challenging problem, with the main difficulties being high-dimensional data and the collection of patient samples. Concretely, obtaining patient samples is a costly and resource-intensive process, and imbalances often exist between sa...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-024-06013-z |
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