Variational graph autoencoders for multiview canonical correlation analysis

Kaloga, Yacouba; Borgnat, Pierre; Chepuri, Sundeep Prabhakar; Abry, Patrice; Habrard, Amaury

doi:10.1016/j.sigpro.2021.108182

Cited by 12 publications

(1 citation statement)

References 17 publications

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“…For MVAE method, the multi-omics fusion is achieved by the product of latent distributions from the existing omics layers. Though MVAE employed 'view dropout' during the model training 56 , the features for missing omics layers were not recovered which lowered the performance for the downstream classification tasks. For KIPAN dataset, the achieved ACCs of CLCLSA were higher than 88% even using the multi-omics data with the missing rate of 0.8.…”

Section: Performance Using Incomplete Multi-omics Datamentioning

confidence: 99%

CLCLSA: Cross-omics Linked embedding with Contrastive Learning and Self Attention for multi-omics integration with incomplete multi-omics data

Zhao

Liu

Zhang

et al. 2023

Preprint

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Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding genetic data. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning, which is used to maximize the mutual information between different types of omics, is employed before latent feature concatenation. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicated that the proposed CLCLSA outperformed the state-of-the-art approaches for multi-omics data classification using incomplete multiomics data.

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Section: Performance Using Incomplete Multi-omics Datamentioning

confidence: 99%