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深度整合潜伏一致表示在高噪音多omics数据的癌症亚型的癌症亚型

Yueyi Cai1, Shunfang Wang1

  • 1Department of Computer Science and Engineering, School of Information Science and Engineering, Yunnan University, Kunming, 650504, Yunnan, China.

Briefings in bioinformatics
|March 1, 2024
PubMed
概括

本研究介绍了深度整合潜伏一致表示 (DILCR),这是一种使用多omics数据进行癌症亚型识别的深度学习模型. DILCR有效地整合了奥米克信息,提高了癌症分类的准确性和生物解释性.

关键词:
癌症亚型的分类相反的学习学习学习.多主题数据集成数据集成.变量自动编码器变量自动编码器

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