A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders-Reference-Cited by-同舟云学术

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A Innovative Strategy for Identifying Subtypes Through the Analysis of Multi-Omics Data with Adversarial Autoencoders

Published:2025-06-26 Issue: Volume: Page:
ISSN:1557-8666
Container-title:Journal of Computational Biology
language:en
Short-container-title:Journal of Computational Biology

Author:

Chen Xia¹,Nie Hao²,Chen Quanwei³,Zhang Xiang²,He Zixing²,Chao Xiuxiu²,Ou Weihao²,Fu Xiangzheng²,Chen Haowen²^ORCID

Affiliation:

1. Aeronautical Electronic Equipment Maintenance College, Changsha Aeronautical Vocational and Technical College, Changsha, China.

2. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.

3. Hunan Skynet Digital Forensic Center, China.

Publisher

Mary Ann Liebert Inc

Link

https://www.liebertpub.com/doi/pdf/10.1089/cmb.2024.0927

Reference59 articles.

1. Tyrosine kinase signalling in breast cancer: Epidermal growth factor receptor and c-Src interactions in breast cancer

2. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

3. A fuzzy extension of the silhouette width criterion for cluster analysis

4. Deep Learning–Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer

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