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Gene expression data analysis for classification of bipolar disorders

Leska Valsamo, Bei Aikaterini, Petrakis Evripidis, Zervakis Michail

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URIhttp://purl.tuc.gr/dl/dias/9CE41ADE-C233-4C3B-865E-EEFCFF858D76-
Αναγνωριστικόhttps://doi.org/10.1007/978-3-319-32703-7_97-
Αναγνωριστικόhttps://link.springer.com/chapter/10.1007/978-3-319-32703-7_97-
Γλώσσαen-
Μέγεθος7 pagesen
ΤίτλοςGene expression data analysis for classification of bipolar disordersen
ΔημιουργόςLeska Valsamoen
ΔημιουργόςΛεσκα Βαλσαμωel
ΔημιουργόςBei Aikaterinien
ΔημιουργόςΜπεη Αικατερινηel
ΔημιουργόςPetrakis Evripidisen
ΔημιουργόςΠετρακης Ευριπιδηςel
ΔημιουργόςZervakis Michailen
ΔημιουργόςΖερβακης Μιχαηλel
ΕκδότηςSpringer Verlagen
ΠερίληψηIn 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
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2018-10-26-
Ημερομηνία Δημοσίευσης2016-
Θεματική ΚατηγορίαBipolar disorderen
Θεματική ΚατηγορίαMicroarray analysisen
Θεματική ΚατηγορίαRecursive feature eliminationen
Θεματική ΚατηγορίαSignificance analysis of Microarraysen
Θεματική ΚατηγορίαSupport & relevance vector machinesen
Βιβλιογραφική ΑναφοράV. 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_96en

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