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Technical University of Crete
Technical University of Crete
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| URI | http://purl.tuc.gr/dl/dias/1DCC5A2B-CCD5-4B80-8E61-12EF753DDEA1 | - |
| Identifier | https://doi.org/10.1109/BHI56158.2022.9926949 | - |
| Identifier | https://ieeexplore.ieee.org/document/9926949 | - |
| Language | en | - |
| Extent | 5 pages | en |
| Title | A 21-hub-gene signature in multiple sclerosis identified using machine learning techniques | en |
| Creator | Tsakaneli Stavroula | en |
| Creator | Τσακανελη Σταυρουλα | el |
| Creator | Bei Aikaterini | en |
| Creator | Μπεη Αικατερινη | el |
| Creator | Zervakis Michail | en |
| Creator | Ζερβακης Μιχαηλ | el |
| Publisher | Institute of Electrical and Electronics Engineers | en |
| Content Summary | Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease that affects approximately 2.8 million persons globally. While there is currently no cure for this neurodegenerative disease, MS has become a highly manageable disease through treatment options like disease-modifying medications, that can help to control the symptoms and slow disease progression. Among them, interferon beta (IFNβ) therapy is a first-line treatment for MS but has shown to be only partially effective. Thus, it is important to identify biomarkers that aid in early identification of IFNβ responders. In this study, based on gene expression profiles from untreated and interferon treated patients from a publicly available dataset, we performed differential expression analysis and Pigengene network association (weighted correlation network analysis (WGCNA) and Bayesian networks modeling) in order to construct a high-confidence protein-protein (PPI) interaction network. Subsequently, aiming to find the most significant clustering modules and hub genes, we applied a number of topological analysis methods (cytoHubba plugin) followed by MCODE clustering algorithm. Our approach resulted in highly connected hub genes generating a reliable 21-hubgene signature that could predict the response of interferon beta (IFNβ) therapy in patients with MS. The 21-hub-gene signature showed high classification performance (Accuracy = 91,49%, Sensitivity = 94.55%, Specificity = 87.15%) demonstrating potential clinical benefit. | en |
| Type of Item | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Type of Item | Conference Full Paper | en |
| License | http://creativecommons.org/licenses/by/4.0/ | en |
| Date of Item | 2024-08-02 | - |
| Date of Publication | 2022 | - |
| Subject | Differentially expressed genes (DEGs) | en |
| Subject | LIMMA | en |
| Subject | SAM | en |
| Subject | Weighted correlation network analysis (WGCNA) | en |
| Subject | Bayesian networks | en |
| Subject | Pigengene | en |
| Subject | cytoHubba | en |
| Subject | MCODE | en |
| Subject | Interferon beta (INFβ) | en |
| Subject | Multiple sclerosis (MS) | en |
| Bibliographic Citation | S. Tsakaneli, E. S. Bei and M. E. Zervakis, "A 21-hub-gene signature in multiple sclerosis identified using machine learning techniques," in Proceedings of the 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI 2022), Ioannina, Greece, 2022, doi: 10.1109/BHI56158.2022.9926949. | en |
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