Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/9CE41ADE-C233-4C3B-865E-EEFCFF858D76 | - |
| Αναγνωριστικό | https://doi.org/10.1007/978-3-319-32703-7_97 | - |
| Αναγνωριστικό | https://link.springer.com/chapter/10.1007/978-3-319-32703-7_97 | - |
| Γλώσσα | en | - |
| Μέγεθος | 7 pages | en |
| Τίτλος | Gene expression data analysis for classification of bipolar disorders | en |
| Δημιουργός | Leska Valsamo | en |
| Δημιουργός | Λεσκα Βαλσαμω | el |
| Δημιουργός | Bei Aikaterini | en |
| Δημιουργός | Μπεη Αικατερινη | el |
| Δημιουργός | Petrakis Evripidis | en |
| Δημιουργός | Πετρακης Ευριπιδης | el |
| Δημιουργός | Zervakis Michail | en |
| Δημιουργός | Ζερβακης Μιχαηλ | el |
| Εκδότης | Springer Verlag | en |
| Περίληψη | In recent years DNA microarray technology has become a widely used tool for gene expression profile analysis. This technology can be useful for the early diagnosis of complex diseases such as bipolar disorder, providing useful information for its genetic background. The ability to classify bipolar disorders may have a major impact on our understanding of disease pathophysiology, as well as it may be essential for guiding the appropriate treatment strategy and determining prognosis for successful targeted therapy. In this preliminary meta-data-study, we propose an analytic framework for biomarker identification aiming at prediction of bipolar disorder, by considering peripheral gene expression differences between bipolar patients and healthy controls. The aim of this paper is to extract a significant genomic signature for which biological knowledge may already exists and discover novel genomic information that can motivate further analysis. We study two classification algorithms based on support and relevance vector machines. The observed results indicate that the latter approach performs better in the specific biological environment. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2018-10-26 | - |
| Ημερομηνία Δημοσίευσης | 2016 | - |
| Θεματική Κατηγορία | Bipolar disorder | en |
| Θεματική Κατηγορία | Microarray analysis | en |
| Θεματική Κατηγορία | Recursive feature elimination | en |
| Θεματική Κατηγορία | Significance analysis of Microarrays | en |
| Θεματική Κατηγορία | Support & relevance vector machines | en |
| Βιβλιογραφική Αναφορά | V. Leska, E. S. Bei, E. Petrakis and M. Zervakis, "Gene expression data analysis for classification of bipolar disorders," in 14th Mediterranean Conference on Medical and Biological Engineering and Computing, 2016, pp. 494-500. doi: 10.1007/978-3-319-32703-7_96 | en |
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