Study on preprocessing and classifying mass spectral raw data concerning human normal and disease cases

Floros, XE; Spyrou, GM; Vougas, KN; Tsangaris, GT; Nikita, KS

dc.contributor.author	Floros, XE	en
dc.contributor.author	Spyrou, GM	en
dc.contributor.author	Vougas, KN	en
dc.contributor.author	Tsangaris, GT	en
dc.contributor.author	Nikita, KS	en
dc.date.accessioned	2014-03-01T02:44:12Z
dc.date.available	2014-03-01T02:44:12Z
dc.date.issued	2006	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31748
dc.subject	Biomarkers	en
dc.subject	Classification	en
dc.subject	Early diagnosis	en
dc.subject	Feature extraction	en
dc.subject	Mass spectra preprocessing	en
dc.subject	Ovarian cancer	en
dc.subject.other	Acoustic noise	en
dc.subject.other	Algorithms	en
dc.subject.other	Biomarkers	en
dc.subject.other	Feature extraction	en
dc.subject.other	Mass spectrometry	en
dc.subject.other	Plasma (human)	en
dc.subject.other	Redundancy	en
dc.subject.other	Support vector machines	en
dc.subject.other	Tissue engineering	en
dc.subject.other	Wavelet transforms	en
dc.subject.other	Biological fluids	en
dc.subject.other	Ovarian cancer	en
dc.subject.other	Shift-Invariant Discrete Wavelet Transform (SIDWT)	en
dc.subject.other	Biological systems	en
dc.title	Study on preprocessing and classifying mass spectral raw data concerning human normal and disease cases	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/11946465_35	en
heal.identifier.secondary	http://dx.doi.org/10.1007/11946465_35	en
heal.publicationDate	2006	en
heal.abstract	Mass spectrometry is becoming an important tool in biological sciences. Tissue samples or easily obtained biological fluids (serum, plasma, urine) are analysed by a variety of mass spectrometry methods, producing spectra characterized by very high dimensionality and a high level of noise. Here we address a feature exraction method for mass spectra which consists of two main steps : In the first step an algorithm for low level preprocessing of mass spectra is applied, including denoising with the Shift-Invariant Discrete Wavelet Transform (SIDWT), smoothing, baseline correction, peak detection and normalization of the resulting peak-lists. After this step, we claim to have reduced dimensionality and redundancy of the initial mass spectra representation while keeping all the meaningful features (potential biomarkers) required for disease related proteomic patterns to be identified. In the second step, the peak-lists are alligned and fed to a Support Vector Machine (SVM) which classifies the mass spectra. This procedure was applied to SELDI-QqTOF spectral data collected from normal and ovarian cancer serum samples. The classification performance was assessed for distinct values of the parameters involved in the feature extraction pipeline. The method described here for low-level preprocessing of mass spectra results in 98.3% sensitivity, 98.3% specificity and an AUC (Area Under Curve) of 0.981 in spectra classification. © Springer-Verlag Berlin Heidelberg 2006.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/11946465_35	en
dc.identifier.volume	4345 LNBI	en
dc.identifier.spage	390	en
dc.identifier.epage	401	en