An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra

dc.contributor.author	Μπουγιούκος, Παναγιώτης	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-14T11:43:16Z
dc.date.available	2015-05-14T11:43:16Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10360
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.cmpbjournal.com/article/S0169-2607(09)00293-4/abstract	en
dc.source	http://www.sciencedirect.com/science/article/pii/S0169260709002934	en
dc.subject	Probabilistic neural network
dc.subject	Support vector machines
dc.subject	Πιθανολογικό νευρωνικό δίκτυο
dc.subject	Μηχανές διανυσμάτων υποστήριξης
dc.title	An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Biomedical engineering
heal.classification	Τεχνολογία
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2008009003
heal.contributorName	Κωστόπουλος, Σπυρίδων	el
heal.contributorName	Νικηφορίδης, Γεώργιος Σ.	el
heal.contributorName	Μπεζεριάνος, Αναστάσιος	el
heal.identifier.secondary	DOI: http://dx.doi.org/10.1016/j.cmpb.2009.11.003
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2010
heal.bibliographicCitation	Bougioukos, P., Glotsos, D., Cavouras, D., Daskalakis, A., Kalatzis, I., et al. (August 2010). An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra. Computer Methods and Programs in Biomedicine. 99(2). pp. 147–153. Elsevier Ireland Ltd: 2010. Available from: http://www.sciencedirect.com/science/article/pii/S0169260709002934 [Accessed 09/12/2009]	en
heal.abstract	In this study, a pattern recognition system is presented for improving the classification accuracy of MS-spectra by means of gathering information from different MS-spectra intensity regions using a majority vote ensemble combination. The method starts by automatically breaking down all MS-spectra into common intensity regions. Subsequently, the most informative features (m/z values), which might constitute potential significant biomarkers, are extracted from each common intensity region over all the MS-spectra and, finally, normal from ovarian cancer MS-spectra are discriminated using a multi-classifier scheme, with members the Support Vector Machine, the Probabilistic Neural Network and the k-Nearest Neighbour classifiers. Clinical material was obtained from the publicly available ovarian proteomic dataset (8-7-02). To ensure robust and reliable estimates, the proposed pattern recognition system was evaluated using an external cross-validation process. The average overall performance of the system in discriminating normal from cancer ovarian MS-spectra was 97.18% with 98.52% mean sensitivity and 94.84% mean specificity values.	en
heal.publisher	Elsevier Ireland Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true