Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks
This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine l...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/1/13 |
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| author | Mikhail Krivonosov Tatiana Nazarenko Vadim Ushakov Daniil Vlasenko Denis Zakharov Shangbin Chen Oleg Blyus Alexey Zaikin |
| author_facet | Mikhail Krivonosov Tatiana Nazarenko Vadim Ushakov Daniil Vlasenko Denis Zakharov Shangbin Chen Oleg Blyus Alexey Zaikin |
| author_sort | Mikhail Krivonosov |
| collection | DOAJ |
| description | This paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle 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, utilising 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 findings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems. |
| format | Article |
| id | doaj-art-51d91438c7ca40d68b42e16fe0c1c041 |
| institution | Kabale University |
| issn | 2227-7080 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-51d91438c7ca40d68b42e16fe0c1c0412025-01-24T13:50:44ZengMDPI AGTechnologies2227-70802024-12-011311310.3390/technologies13010013Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural NetworksMikhail Krivonosov0Tatiana Nazarenko1Vadim Ushakov2Daniil Vlasenko3Denis Zakharov4Shangbin Chen5Oleg Blyus6Alexey Zaikin7Laboratory of Systems Medicine of Ageing, Centre for Artificial Intelligence, Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod 603022, RussiaDepartment of Mathematics, Institute for Women’s Health, University College London, London WC1H 0AY, UKInstitute for Cognitive Neuroscience, University Higher School of Economics, 20 Myasnitskaya, Moscow 101000, RussiaInstitute for Cognitive Neuroscience, University Higher School of Economics, 20 Myasnitskaya, Moscow 101000, RussiaInstitute for Cognitive Neuroscience, University Higher School of Economics, 20 Myasnitskaya, Moscow 101000, RussiaBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, ChinaWolfson Institute of Population Health, Queen Mary University of London, London EC1M 6BQ, UKDepartment of Mathematics, Institute for Women’s Health, University College London, London WC1H 0AY, UKThis paper introduces a novel approach for classifying multidimensional physiological and clinical data using Synolitic Graph Neural Networks (SGNNs). SGNNs are particularly good for addressing the challenges posed by high-dimensional datasets, particularly in healthcare, where traditional machine learning and Artificial Intelligence methods often struggle 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, utilising 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 findings highlight the robustness and interpretability of SGNNs in solving complex, high-dimensional classification problems.https://www.mdpi.com/2227-7080/13/1/13networksdata analysisgraph neural networks |
| spellingShingle | Mikhail Krivonosov Tatiana Nazarenko Vadim Ushakov Daniil Vlasenko Denis Zakharov Shangbin Chen Oleg Blyus Alexey Zaikin Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks Technologies networks data analysis graph neural networks |
| title | Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks |
| title_full | Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks |
| title_fullStr | Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks |
| title_full_unstemmed | Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks |
| title_short | Analysis of Multidimensional Clinical and Physiological Data with Synolitical Graph Neural Networks |
| title_sort | analysis of multidimensional clinical and physiological data with synolitical graph neural networks |
| topic | networks data analysis graph neural networks |
| url | https://www.mdpi.com/2227-7080/13/1/13 |
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