Classification of mass spectrometry based protein markers by kriging error matching

Tuan D. Pham; Honghui Wang; Xiaobo Zhou; Dominik Beck; Miriam Brandl; Gerard Hoehn; Joseph Azok; Marie Luise Brennan; Stanley L. Hazen; Stephen T.C. Wong

doi:10.1007/978-3-540-70715-8_8

Classification of mass spectrometry based protein markers by kriging error matching

Tuan D. Pham, Honghui Wang, Xiaobo Zhou, Dominik Beck, Miriam Brandl, Gerard Hoehn, Joseph Azok, Marie Luise Brennan, Stanley L. Hazen, Stephen T.C. Wong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.

Original language	English (US)
Title of host publication	Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings
Pages	82-94
Number of pages	13
DOIs	https://doi.org/10.1007/978-3-540-70715-8_8
State	Published - 2008
Event	3rd International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry, MDA 2008 - Leipzig, Germany Duration: Jul 14 2008 → Jul 14 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5108 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry, MDA 2008
Country/Territory	Germany
City	Leipzig
Period	7/14/08 → 7/14/08

Keywords

Bioinformatics
Biomarker discovery
Geostatistics
Mass spectrometry data
Pattern classification
Proteomics

ASJC Scopus subject areas

General Computer Science
General Biochemistry, Genetics and Molecular Biology
Theoretical Computer Science

Access to Document

10.1007/978-3-540-70715-8_8

Cite this

Pham, T. D., Wang, H., Zhou, X., Beck, D., Brandl, M., Hoehn, G., Azok, J., Brennan, M. L., Hazen, S. L., & Wong, S. T. C. (2008). Classification of mass spectrometry based protein markers by kriging error matching. In Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings (pp. 82-94). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5108 LNAI). https://doi.org/10.1007/978-3-540-70715-8_8

Classification of mass spectrometry based protein markers by kriging error matching. / Pham, Tuan D.; Wang, Honghui; Zhou, Xiaobo et al.
Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings. 2008. p. 82-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5108 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pham, TD, Wang, H, Zhou, X, Beck, D, Brandl, M, Hoehn, G, Azok, J, Brennan, ML, Hazen, SL & Wong, STC 2008, Classification of mass spectrometry based protein markers by kriging error matching. in Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5108 LNAI, pp. 82-94, 3rd International Conference on Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry, MDA 2008, Leipzig, Germany, 7/14/08. https://doi.org/10.1007/978-3-540-70715-8_8

Pham TD, Wang H, Zhou X, Beck D, Brandl M, Hoehn G et al. Classification of mass spectrometry based protein markers by kriging error matching. In Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings. 2008. p. 82-94. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-70715-8_8

Pham, Tuan D. ; Wang, Honghui ; Zhou, Xiaobo et al. / Classification of mass spectrometry based protein markers by kriging error matching. Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry - Third International Conference, MDA 2008, Proceedings. 2008. pp. 82-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Hoehn, Gerard

AU - Azok, Joseph

AU - Brennan, Marie Luise

AU - Hazen, Stanley L.

AU - Wong, Stephen T.C.

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AB - Discovery of biomarkers using serum proteomic patterns is currently one of the most attractive interdisciplinary research areas in computational life science. This new proteomic approach has the clinical significance in being able to detect disease in its early stages and to develop new drugs for disease treatment and prevention. This paper introduces a novel pattern classification strategy for identifying protein biomarkers using mass spectrometry data of blood samples collected from patients in emergency department monitored for major adverse cardiac events within six months. We applied the theory of geostatistics and a kriging error matching scheme for identifying protein biomarkers that are able to provide an average classification rate superior to other current methods. The proposed strategy is very promising as a general computational bioinformatic model for proteomic-pattern based biomarker discovery.

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Classification of mass spectrometry based protein markers by kriging error matching

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