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  <doc>
    <id>6058</id>
    <completedYear/>
    <publishedYear>2009</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>189</pageFirst>
    <pageLast>199</pageLast>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
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    <title language="eng">Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform</title>
    <parentTitle language="eng">Computing and Visualization in Science</parentTitle>
    <enrichment key="opus.import.data">@ARTICLESchleif2009a, Author = F.-M. Schleif and M. Lindemann and P. Maass and M. Diaz and J. Decker and T. Elssner and M. Kuhn and H. Thiele, Title = Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform, Pages = 189–199, Volume = 12, journal= Computing and Visualization in Science,abstract = Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treat- ment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parametrization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov- Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the ClinProt-System1 [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coeffi- cients where better than those obtained from the peak areas. , Year = 2009</enrichment>
    <enrichment key="opus.import.dataHash">md5:2e5c61b6c6364b69d7f4de46460e67fd</enrichment>
    <enrichment key="opus.import.date">2025-07-15T08:47:15+00:00</enrichment>
    <enrichment key="opus.import.file">/tmp/phpzbxNGL</enrichment>
    <enrichment key="opus.import.format">bibtex</enrichment>
    <enrichment key="opus.import.id">6876159341a060.14275529</enrichment>
    <enrichment key="review.accepted_by">2</enrichment>
    <author>Frank-Michael Schleif</author>
    <author>M. Lindemann</author>
    <author>P. Maass</author>
    <author>M. Diaz</author>
    <author>J. Decker</author>
    <author>T. Elssner</author>
    <author>M. Kuhn</author>
    <author>H. Thiele</author>
  </doc>
</export-example>
