AEGAN-Pathifier: a data augmentation method to improve cancer classification for imbalanced gene expression data

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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Bibliographic Details
Main Authors: Qiaosheng Zhang, Yalong Wei, Jie Hou, Hongpeng Li, Zhaoman Zhong
Format: Article
Language:English
Published: BMC 2024-12-01
Series:BMC Bioinformatics
Subjects:
Pathway
Deep learning
Pathifier
Generative adversarial network
Imbalanced data
Online Access:https://doi.org/10.1186/s12859-024-06013-z
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https://doi.org/10.1186/s12859-024-06013-z

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