MGDMCL: A multi-omics integration framework based on masked graph dynamic learning and multi-granularity feature contrastive learning for biomedical classification

•A novel multi-omics integration framework termed MGDMCL is introduced.•Proposed a masked graph dynamic learning method to obtain omics-specific feature representations.•Devised a multi-granularity feature contrastive learning approach to learn consensus feature representations.•MGDMCL outperforms s...

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Bibliographic Details
Published in:	Computer methods and programs in biomedicine Vol. 271; p. 109024
Main Authors:	Chen, Wengxiang, Qiu, Hang
Format:	Journal Article
Language:	English
Published:	Ireland Elsevier B.V 01.11.2025
Subjects:	Algorithms Biomedical classification Computational Biology - methods Computer Graphics Contrastive learning Genomics Graph convolutional networks Humans Machine Learning Masked graph autoencoder Multi-omics integration Multiomics Neural Networks, Computer Precision Medicine Masked graph autoencoder Biomedical classification Graph convolutional networks Multi-omics integration Contrastive learning
ISSN:	0169-2607, 1872-7565, 1872-7565
Online Access:	Get full text
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