MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

MOCAT: multi-omics integration with auxiliary classifiers enhanced autoencoder

Abstract Background Integrating multi-omics data is emerging as a critical approach in enhancing our understanding of complex diseases. Innovative computational methods capable of managing high-dimensional and heterogeneous datasets are required to unlock the full potential of such rich and diverse...

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Bibliographic Details
Main Authors:	Xiaohui Yao, Xiaohan Jiang, Haoran Luo, Hong Liang, Xiufen Ye, Yanhui Wei, Shan Cong
Format:	Article
Language:	English
Published:	BMC 2024-03-01
Series:	BioData Mining
Subjects:	Multi-omics integration Auxiliary classifier Autoencoder Attention mechanism Trustworthy learning Disease prediction
Online Access:	https://doi.org/10.1186/s13040-024-00360-6

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