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Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

Vavouraki, N. ORCID:, Tomkins, J. E. ORCID:, Kara, E. ORCID:, Houlden, H., Hardy, J., Tindall, M. J., Lewis, P. A. ORCID: and Manzoni, C. (2021) Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias. iScience, 24 (5). 102484. ISSN 2589-0042

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To link to this item DOI: 10.1016/j.isci.2021.102484


The Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Owing to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human data were used to create a protein-protein interaction network based on the causative genes. Network evaluation as a combination of topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes, such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, indicating that there is scope to further classify conditions currently described under the same umbrella-term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

Item Type:Article
Divisions:Interdisciplinary centres and themes > Institute for Cardiovascular and Metabolic Research (ICMR)
Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Life Sciences > School of Chemistry, Food and Pharmacy > School of Pharmacy > Division of Pharmacology
ID Code:99186
Publisher:Cell Press


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