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| URI | http://purl.tuc.gr/dl/dias/1DCC5A2B-CCD5-4B80-8E61-12EF753DDEA1 | - |
| Αναγνωριστικό | https://doi.org/10.1109/BHI56158.2022.9926949 | - |
| Αναγνωριστικό | https://ieeexplore.ieee.org/document/9926949 | - |
| Γλώσσα | en | - |
| Μέγεθος | 5 pages | en |
| Τίτλος | A 21-hub-gene signature in multiple sclerosis identified using machine learning techniques | en |
| Δημιουργός | Tsakaneli Stavroula | en |
| Δημιουργός | Τσακανελη Σταυρουλα | el |
| Δημιουργός | Bei Aikaterini | en |
| Δημιουργός | Μπεη Αικατερινη | el |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | 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 |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2024-08-02 | - |
| Ημερομηνία Δημοσίευσης | 2022 | - |
| Θεματική Κατηγορία | Differentially expressed genes (DEGs) | en |
| Θεματική Κατηγορία | LIMMA | en |
| Θεματική Κατηγορία | SAM | en |
| Θεματική Κατηγορία | Weighted correlation network analysis (WGCNA) | en |
| Θεματική Κατηγορία | Bayesian networks | en |
| Θεματική Κατηγορία | Pigengene | en |
| Θεματική Κατηγορία | cytoHubba | en |
| Θεματική Κατηγορία | MCODE | en |
| Θεματική Κατηγορία | Interferon beta (INFβ) | en |
| Θεματική Κατηγορία | Multiple sclerosis (MS) | en |
| Βιβλιογραφική Αναφορά | 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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