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

 
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2010 (EN)
An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra (EN)

Μπουγιούκος, Παναγιώτης (EL)
Δασκαλάκης, Αντώνης (EL)
Γκλώτσος, Δημήτριος (EL)
Κάβουρας, Διονύσης Α. (EL)
Καλατζής, Ιωάννης (EL)

Νικηφορίδης, Γεώργιος Σ. (EL)
Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε. (EL)
Κωστόπουλος, Σπυρίδων (EL)
Μπεζεριάνος, Αναστάσιος (EL)

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)

journalArticle

Support vector machines (EN)
Probabilistic neural network (EN)
Πιθανολογικό νευρωνικό δίκτυο (EN)
Μηχανές διανυσμάτων υποστήριξης (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

Computer Methods and Programs in Biomedicine (EN)

English

2010

DOI: http://dx.doi.org/10.1016/j.cmpb.2009.11.003

Elsevier Ireland Ltd (EN)



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