一个基于对比学习的深度神经网络,通过整合多omics数据来进行癌症亚型识别
Hua Chai1, Weizhen Deng1, Junyu Wei1
1School of Mathematics and Big Data, Foshan University, Foshan, 528000, China.
Interdisciplinary sciences, computational life sciences
|September 4, 2024
概括
这项研究引入了一种新的深度学习方法,用于使用多omics数据进行癌症亚型识别. 该方法准确地识别了不同的癌症亚型,改善了患者分层和预后预测.
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.
- 基因组学就是基因组学.
背景情况:
- 准确的癌症亚型确定对于预后和个性化治疗至关重要.
- 多omics数据提供了洞察力,但面临着诸如高维度和小样本大小等挑战,导致模两可的亚型.
- 现有的集群方法在与重叠的癌症亚型作斗争.
研究的目的:
- 为癌症亚型开发一种基于对比学习的新型深度学习方法.
- 为了应对高维度和小样本大小在患者集群的多omics数据中的挑战.
- 提高已识别的癌症亚型的准确性和生物学意义.
主要方法:
- 提出了一种端到端的深度学习方法,利用对比学习和自我监督学习.
- 将该方法应用于9个公共癌症数据集,用于患者集群.
- 开发了一个XGBoost分类模型来评估不同omics数据类型的重要性.
主要成果:
- 与现有方法相比,拟议的方法在分离具有不同生存结果 (p < 0.05) 的患者方面表现优越.
- mRNA被确定为癌症存活的最重要的奥米克特征,其次是DNA甲基化和miRNA.
- 一个案例研究成功地聚集了亚型,并确定了14个与癌症相关的基因,其中12个 (85.7%) 被文献验证.
结论:
- 开发的方法有效地识别出统计学和生物学上显著的癌症亚型.
- 这种方法增强了患者分层和个性化医疗策略.
- 这项研究提供了一个宝贵的工具,用于使用多omics数据进行癌症分子亚型化.
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