Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks

Technologies. 2025. Vol. 13. No. 1. Article 13.

Krivonosov M., Nazarenko T., Ushakov V., Vlasenko D., Zakharov D., Shangbin C., Blyus O., Zaikin A.

This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good forto addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional mMachine lLearning and Artificial Intelligence methods often struggles to find global optima due to the “curse of dimensionality”. To apply Geometric Deep Learning we propose a synolitic or ensemble graph representation of the data, a universal method that transforms any multidimensional dataset into a network, utiliszing only class labels from training data. The paper demonstrates the effectiveness of this approach through two classification tasks: synthetic and fMRI data from cognitive tasks. Convolutional Graph Neural Network architecture is then applied, and the results are compared with established machine learning algorithms. The fFindings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems

Keywords: анализ данных, сети, data analysis, Networks, графовые нейронные сети, Graph neural networks

Publication based on the results of: Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)