Omics 数据的图形表示学习策略:帕金森病案例研究

Elisa Gómez de Lope, Saurabh Deshpande, Ramón Viñas Torné, Pietro Liò, Enrico Glaab, Stéphane P. A. Bordas
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引用次数: 0

摘要

Omics 数据分析对研究复杂疾病至关重要,但其高维性和异质性对传统的统计和机器学习方法提出了挑战。图神经网络作为一种有前途的替代方法已经出现,但在实际生物医学挑战中设计和优化图神经网络的最佳策略仍不明确。本研究利用帕金森病和对照样本的高通量生物数据,评估了用于病例对照分类的各种图表示学习模型。我们比较了从样本相似性网络和分子相互作用网络(包括蛋白质-蛋白质和代谢物-代谢物相互作用(PPI、MMI))中得出的拓扑结构。我们评估了图卷积网络(GCNs)、切比雪夫谱图卷积(ChebyNet)和图注意网络(GAT),以及图变换器、图 U-net 等先进架构和多层感知器(MLP)等简化模型。这些模型被系统地独立应用于转录组学和代谢组学数据。我们的比较分析强调了各种架构在从 omics 数据中提取模式方面的优势和局限性,为生物医学研究中建立更准确、更可解释的模型铺平了道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease
Omics data analysis is crucial for studying complex diseases, but its high dimensionality and heterogeneity challenge classical statistical and machine learning methods. Graph neural networks have emerged as promising alternatives, yet the optimal strategies for their design and optimization in real-world biomedical challenges remain unclear. This study evaluates various graph representation learning models for case-control classification using high-throughput biological data from Parkinson's disease and control samples. We compare topologies derived from sample similarity networks and molecular interaction networks, including protein-protein and metabolite-metabolite interactions (PPI, MMI). Graph Convolutional Network (GCNs), Chebyshev spectral graph convolution (ChebyNet), and Graph Attention Network (GAT), are evaluated alongside advanced architectures like graph transformers, the graph U-net, and simpler models like multilayer perceptron (MLP). These models are systematically applied to transcriptomics and metabolomics data independently. Our comparative analysis highlights the benefits and limitations of various architectures in extracting patterns from omics data, paving the way for more accurate and interpretable models in biomedical research.
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