A clustering based hybrid system for mass spectrometry data analysis

Yang, Pengyi and Zhang, Zili 2008, A clustering based hybrid system for mass spectrometry data analysis, Lecture notes in computer science, vol. 5265, pp. 98-109, doi: 10.1007/978-3-540-88436-1.

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Title A clustering based hybrid system for mass spectrometry data analysis
Author(s) Yang, Pengyi
Zhang, ZiliORCID iD for Zhang, Zili orcid.org/0000-0002-8721-9333
Journal name Lecture notes in computer science
Volume number 5265
Start page 98
End page 109
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Publication date 2008-10
ISSN 0302-9743
Summary Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.
Language eng
DOI 10.1007/978-3-540-88436-1
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, Springer-Verlag
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017961

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