AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selectio...

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Bibliographic Details
Published in:Computers in biology and medicine Vol. 177; p. 108614
Main Authors: Li, Minghe, Guo, Huike, Wang, Keao, Kang, Chuanze, Yin, Yanbin, Zhang, Han
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.07.2024
Elsevier Limited
Subjects:
Algorithms
Biological analysis
Cancer
Classification
Deep Learning
Embedding
Feature importance ranking
Feature selection
Genomics
Heterogeneity
Humans
Internal Medicine
Machine learning
Multi-omics
Multiomics
Neoplasms - classification
Neoplasms - genetics
Neoplasms - metabolism
Other
Pan-cancer classification
Parameters
Precision medicine
Ranking
Variational autoencoders
Deep learning
Variational autoencoders
Pan-cancer classification
Multi-omics
Feature importance ranking
ISSN:0010-4825, 1879-0534, 1879-0534
Online Access:Get full text
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Summary:Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods. •A method is proposed where two phases cooperate to improve the pan-cancer classification performance.•A novel adversarial variational Bayes-based architecture is proposed for multi-omics embedding.•An average gradient Monte Carlo sampling-based mechanism is proposed for stable ranking.•The experimental results show the superior performance on the TCGA pan-cancer dataset.
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ISSN:0010-4825
1879-0534
1879-0534
DOI:10.1016/j.compbiomed.2024.108614