Tao Liang, Xie Yixin, Deng Jeffrey D, Shen Hui, Deng Hong-Wen, Zhou Weihua, Zhao Chen
Department of Computer Science, Kennesaw State University, Marietta, GA 30060.
Department of Information Technology, Kennesaw State University, Marietta, GA, 30060.
ArXiv. 2024 Oct 14:arXiv:2410.11046v1.
Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia, significantly impacting cost, mortality, and burden worldwide. The advent of high-throughput omics technologies, such as genomics, transcriptomics, proteomics, and epigenomics, has revolutionized the molecular understanding of AD. Conventional AI approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis, which are inefficient and may be unnecessary. To reduce the clinical cost and improve the accuracy of AD diagnosis using multi-omics data, we propose a novel staged graph convolutional network with uncertainty quantification (SGUQ). SGUQ begins with mRNA and progressively incorporates DNA methylation and miRNA data only when necessary, reducing overall costs and exposure to harmful tests. Experimental results indicate that 46.23% of the samples can be reliably predicted using only single-modal omics data (mRNA), while an additional 16.04% of the samples can achieve reliable predictions when combining two omics data types (mRNA + DNA methylation). In addition, the proposed staged SGUQ achieved an accuracy of 0.858 on ROSMAP dataset, which outperformed existing methods significantly. The proposed SGUQ can not only be applied to AD diagnosis using multi-omics data, but also has the potential for clinical decision making using multi-viewed data. Our implementation is publicly available at https://github.com/chenzhao2023/multiomicsuncertainty.
阿尔茨海默病（AD）是一种慢性神经退行性疾病，也是痴呆症的主要病因，对全球的成本、死亡率和负担产生了重大影响。高通量组学技术的出现，如基因组学、转录组学、蛋白质组学和表观基因组学，彻底改变了我们对AD的分子理解。传统的人工智能方法通常需要一开始就完成所有组学数据，以实现最佳的AD诊断，这种方法效率低下且可能不必要。为了降低临床成本并提高使用多组学数据进行AD诊断的准确性，我们提出了一种具有不确定性量化的新型分层图卷积网络（SGUQ）。SGUQ从mRNA开始，仅在必要时逐步纳入DNA甲基化和miRNA数据，从而降低总体成本并减少有害检测的风险。实验结果表明，仅使用单模态组学数据（mRNA）就可以可靠地预测46.23%的样本，而在结合两种组学数据类型（mRNA + DNA甲基化）时，另外16.04%的样本可以实现可靠预测。此外，所提出的分层SGUQ在ROSMAP数据集上的准确率达到了0.858，显著优于现有方法。所提出的SGUQ不仅可以应用于使用多组学数据进行AD诊断，还具有使用多视图数据进行临床决策的潜力。我们的实现可在https://github.com/chenzhao2023/multiomicsuncertainty上公开获取。
