Graph-Based Disease Prediction in Neuroimaging: Investigating the Impact of Feature Selection.
| Autor: | Kiakou D; Hellenic Open University, Patra, Greece. kiakoud@cbs.mpg.de.; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. kiakoud@cbs.mpg.de., Adamopoulos A; Hellenic Open University, Patra, Greece.; Democritus University of Thrace, Department of Medicine, Medical Physics Lab, Alexandroupolis, Greece., Scherf N; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.; Center for Scalable Data Analytics and Artificial Intelligence ScaDS.AI, Dresden/Leipzig, Leipzig, Germany. |
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| Jazyk: | angličtina |
| Zdroj: | Advances in experimental medicine and biology [Adv Exp Med Biol] 2023; Vol. 1424, pp. 223-230. |
| DOI: | 10.1007/978-3-031-31982-2_24 |
| Abstrakt: | 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. (© 2023. The Author(s), under exclusive license to Springer Nature Switzerland AG.) |
| Databáze: | MEDLINE |
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