Hellenic Open University, Patra, Greece.
Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
Adv Exp Med Biol. 2023;1424:223-230. doi: 10.1007/978-3-031-31982-2_24.
In biomedical machine learning, data often appear in the form of graphs. Biological systems such as protein interactions and ecological or brain networks are instances of applications that benefit from graph representations. Geometric deep learning is an arising field of techniques that has extended deep neural networks to non-Euclidean domains such as graphs. In particular, graph convolutional neural networks have achieved advanced performance in semi-supervised learning in those domains. Over the last years, these methods have gained traction in neuroscience as they could be the key to a deeper understanding in clinical diagnosis at the systems or network level (for an individual brain but also for across a cohort of subjects). As a proof-of-principle, we study and validate a previous implementation of graph-based semi-supervised classification using a ridge classifier and graph convolutional neural networks. The models are trained on population graphs that integrate imaging and phenotypic information. Our analysis employs neuroimaging data of structural and functional connectivity for prediction of neurodevelopmental and neurodegenerative disorders. Here, we particularly study the effect of different strategies to reduce the dimensionality of the neuroimaging features on the graph nodes on the classification performance.
在生物医学机器学习中，数据通常以图的形式出现。蛋白质相互作用、生态或脑网络等生物系统就是受益于图表示的应用实例。几何深度学习是一种新兴的技术领域，它将深度神经网络扩展到了非欧几里得领域，如图。特别是，图卷积神经网络在这些领域的半监督学习中取得了先进的性能。近年来，这些方法在神经科学中引起了关注，因为它们可能是在系统或网络水平上进行临床诊断的深入理解的关键（对于单个大脑，也可以用于跨队列的受试者）。作为原理验证，我们研究和验证了以前使用岭分类器和图卷积神经网络进行基于图的半监督分类的实现。这些模型是在整合成像和表型信息的群体图上进行训练的。我们的分析利用结构和功能连接的神经影像学数据来预测神经发育和神经退行性疾病。在这里，我们特别研究了不同策略对图节点上神经影像学特征的降维对分类性能的影响。
