脱离对比的多视图聚类与适应性假阴性消除用于癌症亚型
Mengxiang Lin1, Rongqi Fan1, Saisai Zhu1
1School of Computer Science and Artificial Intelligence, Zhengzhou University, Zhengzhou, China.
PLoS computational biology
|December 4, 2025
概括
这项研究引入了解对比多视图集群 (DCMC),这是一个新的自我监督模型,用于使用多omics数据精确地进行癌症亚型. DCMC有效地识别癌症亚型和潜在生物标志物,改进个性化癌症治疗策略.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 癌症异质性要求精确的亚型识别,以有效诊断和个性化治疗.
- 多omics数据集成对于揭示不同的分子特征至关重要.
- 现有的对比集群方法与视图间和视图内关系和错误负面作斗争.
研究的目的:
- 开发一种新的端到端自主监督学习模型,以改善癌症亚型.
- 解决捕获多omics数据关系和处理集群中的虚假负数的局限性.
- 通过精确的亚型和生物标志物发现,增强个性化癌症治疗.
主要方法:
- 拟议的脱对比多视图集群 (DCMC) 模型.
- 采用多视图集群与视图内和视图间的对比学习.
- 引入了自适应的虚假负面消除和伪标签纠正.
主要成果:
- 与19种最先进的方法相比,DCMC在10个癌症数据集上表现出卓越的性能.
- 在肝肝细胞癌中通过差异表达分析确定了潜在的生物标志物.
- 为治疗药物反应验证已识别的癌症亚型.
结论:
- 通过利用多omics数据,DCMC为癌症亚型提供了一种有效的方法.
- 该模型处理虚假负数和完善表示的能力导致更好的集群准确性.
- 这些发现支持DCMC在推进个性化癌症医学方面的潜力.
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