Luo Haoran, Liang Hong, Liu Hongwei, Fan Zhoujie, Wei Yanhui, Yao Xiaohui, Cong Shan
Qingdao Innovation and Development Center, Harbin Engineering University, Qingdao 266000, China.
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.
Int J Mol Sci. 2024 Jan 29;25(3):1655. doi: 10.3390/ijms25031655.
Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.
在推进生物医学研究领域方面，整合的多组学数据在阐明复杂人类疾病方面展现出卓越性能。然而，随着组学信息种类的扩展，由于复杂的相互关系，精确理解组学内部和组学间的信息性变得具有挑战性，从而在多组学数据整合中带来了重大挑战。为解决这一问题，我们引入了一种新颖的多组学整合方法，称为TEMINET。该方法通过利用组学内部共信息表示模块和用于解决组学间融合的可信学习策略来增强诊断预测。考虑到复杂疾病的多因素性质，TEMINET利用组学内部特征构建疾病特异性网络；然后，它应用图注意力网络和多层次框架来捕获比成对关系更多的集体信息性。为了理解组学内部共信息表示的贡献，我们设计了一种可信学习策略来识别整合中每个组学的可靠性。为了整合组学间信息，采用了联合信念融合方法来有效协调不同组学类型的可信表示。我们使用mRNA、甲基化和miRNA数据对四种不同疾病进行的实验表明，TEMINET在分类任务中实现了先进的性能和稳健性。
