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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