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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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06013-z |
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| author | Qiaosheng Zhang Yalong Wei Jie Hou Hongpeng Li Zhaoman Zhong |
| author_facet | Qiaosheng Zhang Yalong Wei Jie Hou Hongpeng Li Zhaoman Zhong |
| author_sort | Qiaosheng Zhang |
| collection | DOAJ |
| description | 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 samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting. Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Network (GAN) to solve these difficulties. Results In this study, we propose an effective and efficient deep learning method, named AEGAN, which combines the capabilities of AutoEncoder and GAN to generate synthetic samples of the minority class in imbalanced gene expression data. The proposed data balancing technique has been demonstrated to be useful for cancer classification and improving the performance of classifier models. Additionally, we integrate prior knowledge from the pathway and employ the pathifier algorithm to calculate pathway scores for each sample. This data augmentation approach, referred to as AEGAN-Pathifier, not only preserves the biological functionality of the data but also possesses dimensional reduction capabilities. Through validation with various classifiers, the experimental results show an improvement in classifier performance. Conclusion AEGAN-Pathifier shows improved performance on the imbalanced datasets GSE25066, GSE20194, BRCA and Liver24. Results from various classifiers indicate that AEGAN-Pathifier has good generalization capability. |
| format | Article |
| id | doaj-art-d4b53c690e4f43568f8eda39953b758f |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-d4b53c690e4f43568f8eda39953b758f2024-12-29T12:49:52ZengBMCBMC Bioinformatics1471-21052024-12-0125111910.1186/s12859-024-06013-zAEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression dataQiaosheng Zhang0Yalong Wei1Jie Hou2Hongpeng Li3Zhaoman Zhong4School of Computer Engineering, Jiangsu Ocean UniversitySchool of Computer Engineering, Jiangsu Ocean UniversityPublic Teaching and Research Department, Huzhou CollegeCollege of Science, Jiangsu Ocean UniversitySchool of Computer Engineering, Jiangsu Ocean UniversityAbstract 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 samples. Moreover, expression data is characterized by high dimensionality, small samples and high noise, which could easily lead to struggles such as dimensionality catastrophe and overfitting. Thus, we incorporate prior knowledge from the pathway and combine AutoEncoder and Generative Adversarial Network (GAN) to solve these difficulties. Results In this study, we propose an effective and efficient deep learning method, named AEGAN, which combines the capabilities of AutoEncoder and GAN to generate synthetic samples of the minority class in imbalanced gene expression data. The proposed data balancing technique has been demonstrated to be useful for cancer classification and improving the performance of classifier models. Additionally, we integrate prior knowledge from the pathway and employ the pathifier algorithm to calculate pathway scores for each sample. This data augmentation approach, referred to as AEGAN-Pathifier, not only preserves the biological functionality of the data but also possesses dimensional reduction capabilities. Through validation with various classifiers, the experimental results show an improvement in classifier performance. Conclusion AEGAN-Pathifier shows improved performance on the imbalanced datasets GSE25066, GSE20194, BRCA and Liver24. Results from various classifiers indicate that AEGAN-Pathifier has good generalization capability.https://doi.org/10.1186/s12859-024-06013-zPathwayDeep learningPathifierGenerative adversarial networkImbalanced data |
| spellingShingle | Qiaosheng Zhang Yalong Wei Jie Hou Hongpeng Li Zhaoman Zhong AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data BMC Bioinformatics Pathway Deep learning Pathifier Generative adversarial network Imbalanced data |
| title | AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data |
| title_full | AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data |
| title_fullStr | AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data |
| title_full_unstemmed | AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data |
| title_short | AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data |
| title_sort | aegan pathifier a data augmentation method to improve cancer classification for imbalanced gene expression data |
| topic | Pathway Deep learning Pathifier Generative adversarial network Imbalanced data |
| url | https://doi.org/10.1186/s12859-024-06013-z |
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