Biomarker selection, employing an iterative peak selection method, and prostate spectra characterization for identifying biomarkers related to prostate cancer

dc.contributor.author	Μπουγιούκος, Παναγιώτης	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Σ.	el
dc.date.accessioned	2015-05-13T07:24:29Z
dc.date.available	2015-05-13T07:24:29Z
dc.date.issued	2015-05-13
dc.identifier.uri	http://hdl.handle.net/11400/10280
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://link.springer.com/chapter/10.1007/978-3-540-74484-9_49	en
dc.subject	Classification
dc.subject	Biomarker selection
dc.subject	Ταξινόμηση
dc.subject	Επιλογή βιοδείκτη
dc.title	Biomarker selection, employing an iterative peak selection method, and prostate spectra characterization for identifying biomarkers related to prostate cancer	en
heal.type	conferenceItem
heal.generalDescription	Proceedings	en
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.contributorName	Μπεζεριάνος, Αναστάσιος	el
heal.identifier.secondary	DOI 10.1007/978-3-540-74484-9_49
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.bibliographicCitation	Bougioukos, P., Cavouras, D., Daskalakis, A., Kalatzis, I., Nikiforidis, G., et al. (2007). Biomarker selection, employing an iterative peak selection method, and prostate spectra characterization for identifying biomarkers related to prostate cancer. Proceedings of the International Conference of Computational Science and Its Applications – ICCSA 2007. Kuala Lumpur, Malaysia, 26th-29th August 2007. pp. 566-574. Springer Berlin Heidelberg: 2007. Available from: http://link.springer.com/chapter/10.1007/978-3-540-74484-9_49	en
heal.abstract	A proteomic analysis system (PAS) for prostate Mass Spectrometry (MS) spectra is proposed for differentiating normal from abnormal and benign from malignant cases and for identifying biomarkers related to prostate cancer. PAS comprised two stages, 1/a pre-processing stage, consisting of MS-spectrum smoothing, normalization, iterative peak selection, and peak alignment, and 2/a classification stage, comprising a 2-level hierarchical tree structure, employing the PNN and SVM classifiers at the 1st (normal-abnormal) and 2nd (benign-malignant) classification levels respectively. PAS first applied local thresholding, for determining the MS-spectrum noise level, and second an iterative global threshold estimation algorithm, for selecting peaks at different intensity ranges. Two optimum sub-sets of these peaks, one at each global threshold, were used to optimally design the hierarchical classification scheme and, thus, indicate the best m/z values. The information rich biomarkers 1160.8, 2082.2, 3595.9, 4275.3, 5817.3, 7653.2, that have been associated with the prostate gland, are proposed for further investigation.	en
heal.publisher	Springer Berlin Heidelberg	en
heal.fullTextAvailability	true
heal.conferenceName	International Conference of Computational Science and Its Applications – ICCSA 2007	en
heal.conferenceItemType	full paper