深度多视图对比学习用于癌症亚型识别
Wenlan Chen1, Hong Wang1, Cheng Liang1
1School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China.
Briefings in bioinformatics
|August 4, 2023
概括
深度多视图对比学习 (DMCL) 从多omics数据中有效识别癌症亚型. 这种方法有助于开发精确的癌症疗法,通过揭示不同的分子概况和潜在的药物反应.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 机器学习在瘤学中
背景情况:
- 癌症异质性为开发精确的治疗策略带来了重大挑战.
- 根据分子概况识别不同的癌症亚型对于有效的临床治疗至关重要.
- 整合多omics数据集用于癌症亚型识别需要先进的计算方法.
研究的目的:
- 提出一种新的自我监督学习模型,深度多视图对比学习 (DMCL),用于准确识别癌症亚型.
- 开发一个端到端的框架,集成重建,对比和集群损失,用于特征表示和集群保存.
- 为了证明DMCL在直接输出癌症亚型方面的能力.
主要方法:
- 开发了深度多视图对比学习 (DMCL),一种自我监督的学习模型.
- 整合重建损失,对比损失和集群损失到一个统一的框架中.
- 评估了10个癌症多组数据集和一个综合数据集的DMCL,与八种替代方法进行比较.
主要成果:
- 与现有方法相比,DMCL在多个数据集的癌症亚型识别方面表现优越.
- 该模型有效地编码样本区分信息,并保留嵌入式特征表示中的集群结构.
- 一个关于肝癌的案例研究表明,鉴定的亚型可能对化疗药物的反应有所不同.
结论:
- DMCL提供了一种强大而高效的计算方法,用于整合多omics数据以识别癌症亚型.
- 这些发现表明,DMCL有可能通过揭示亚型特定药物敏感性来指导个性化癌症治疗策略.
- 这种方法通过从复杂的分子数据中改进亚型发现,推进精密瘤学领域.
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