Tang Xinlu, Guo Rui, Mo Zhanfeng, Fu Wenli, Qian Xiaohua
The Medical Image and Health Informatics Lab, the School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore.
Nat Commun. 2025 Jan 15;16(1):680. doi: 10.1038/s41467-025-56054-y.
Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease. It integrates causal thinking, deep learning, and biological priors to handle non-causal confounding factors, through a contrastive scheme and a spatial-relation regularization that reduces interferences from individual characteristics and noises, respectively. The comprehensive reliability of the proposed method was verified by simulations and applications involving various human diseases, sample origins, and sequencing technologies, highlighting its universal biomedical significance. Overall, this study offers a causal-deep-learning-based perspective with a compatible tool to identify reliable DNAm biomarker candidates, promoting resource-efficient biomarker discovery.
尽管在DNA甲基化（DNAm）生物标志物发现方面有大量数据支持以促进医疗保健研究，但由于初步候选物不可靠以及随后使用昂贵实验进行的补偿，该领域面临巨大的资源障碍。潜在的挑战在于混杂因素，尤其是测量噪声和个体特征。为了可靠地识别用于DNAm生物标志物发现的候选池，我们提出了一种因果驱动的深度正则化框架，以加强与疾病存在因果关系的相关性。它整合了因果思维、深度学习和生物学先验知识，通过一种对比方案和一种空间关系正则化来处理非因果混杂因素，分别减少个体特征和噪声的干扰。通过涉及各种人类疾病、样本来源和测序技术的模拟和应用验证了所提方法的综合可靠性，突出了其普遍的生物医学意义。总体而言，本研究提供了一种基于因果深度学习的视角以及一个兼容工具，以识别可靠的DNAm生物标志物候选物，促进资源高效的生物标志物发现。
