Full-length HPLC signal clustering and biomarker identification in tomato plants

M. Strickert, T. Czauderna, S. Peterek, A. Matros, H. P. Mock, U. Seiffert

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

High resolution HPLC data of a tomato germ plasm collection are studied: Analysis of the molecular constituents of tomato peels from 55 experiments is conducted with focus on the visualization of the plant interrelationships, and on biomarker extraction for the identification of new and highly abundant substances at a wavelength of 280nm. 3000-dimensional chromatogram vectors are processed by state of-the-art and novel methods for baseline correction, data alignment, biomarker retrieval, and data clustering. These processing methods are applied to the tomato data set and the results are presented in a comparative manner, thereby focusing on interesting clusters and retention times of nutritionally valuable tomato lines.

Original languageEnglish
Title of host publicationApplied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006
PublisherWorld Scientific Publishing
Pages549-556
Number of pages8
ISBN (Electronic)9812566902, 9789812566904
DOIs
Publication statusPublished - 1 Jan 2006
Externally publishedYes
EventApplied Artificial Intelligence - 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2006 - Genova, Italy
Duration: 29 Aug 200631 Aug 2006

Conference

ConferenceApplied Artificial Intelligence - 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2006
CountryItaly
CityGenova
Period29/08/0631/08/06

Cite this

Strickert, M., Czauderna, T., Peterek, S., Matros, A., Mock, H. P., & Seiffert, U. (2006). Full-length HPLC signal clustering and biomarker identification in tomato plants. In Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006 (pp. 549-556). World Scientific Publishing. https://doi.org/10.1142/9789812774118_0078
Strickert, M. ; Czauderna, T. ; Peterek, S. ; Matros, A. ; Mock, H. P. ; Seiffert, U. / Full-length HPLC signal clustering and biomarker identification in tomato plants. Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006. World Scientific Publishing, 2006. pp. 549-556
@inproceedings{3f57cbdff006484b8f614bd16246ef16,
title = "Full-length HPLC signal clustering and biomarker identification in tomato plants",
abstract = "High resolution HPLC data of a tomato germ plasm collection are studied: Analysis of the molecular constituents of tomato peels from 55 experiments is conducted with focus on the visualization of the plant interrelationships, and on biomarker extraction for the identification of new and highly abundant substances at a wavelength of 280nm. 3000-dimensional chromatogram vectors are processed by state of-the-art and novel methods for baseline correction, data alignment, biomarker retrieval, and data clustering. These processing methods are applied to the tomato data set and the results are presented in a comparative manner, thereby focusing on interesting clusters and retention times of nutritionally valuable tomato lines.",
author = "M. Strickert and T. Czauderna and S. Peterek and A. Matros and Mock, {H. P.} and U. Seiffert",
year = "2006",
month = "1",
day = "1",
doi = "10.1142/9789812774118_0078",
language = "English",
pages = "549--556",
booktitle = "Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006",
publisher = "World Scientific Publishing",
address = "Singapore",

}

Strickert, M, Czauderna, T, Peterek, S, Matros, A, Mock, HP & Seiffert, U 2006, Full-length HPLC signal clustering and biomarker identification in tomato plants. in Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006. World Scientific Publishing, pp. 549-556, Applied Artificial Intelligence - 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2006, Genova, Italy, 29/08/06. https://doi.org/10.1142/9789812774118_0078

Full-length HPLC signal clustering and biomarker identification in tomato plants. / Strickert, M.; Czauderna, T.; Peterek, S.; Matros, A.; Mock, H. P.; Seiffert, U.

Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006. World Scientific Publishing, 2006. p. 549-556.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

TY - GEN

T1 - Full-length HPLC signal clustering and biomarker identification in tomato plants

AU - Strickert, M.

AU - Czauderna, T.

AU - Peterek, S.

AU - Matros, A.

AU - Mock, H. P.

AU - Seiffert, U.

PY - 2006/1/1

Y1 - 2006/1/1

N2 - High resolution HPLC data of a tomato germ plasm collection are studied: Analysis of the molecular constituents of tomato peels from 55 experiments is conducted with focus on the visualization of the plant interrelationships, and on biomarker extraction for the identification of new and highly abundant substances at a wavelength of 280nm. 3000-dimensional chromatogram vectors are processed by state of-the-art and novel methods for baseline correction, data alignment, biomarker retrieval, and data clustering. These processing methods are applied to the tomato data set and the results are presented in a comparative manner, thereby focusing on interesting clusters and retention times of nutritionally valuable tomato lines.

AB - High resolution HPLC data of a tomato germ plasm collection are studied: Analysis of the molecular constituents of tomato peels from 55 experiments is conducted with focus on the visualization of the plant interrelationships, and on biomarker extraction for the identification of new and highly abundant substances at a wavelength of 280nm. 3000-dimensional chromatogram vectors are processed by state of-the-art and novel methods for baseline correction, data alignment, biomarker retrieval, and data clustering. These processing methods are applied to the tomato data set and the results are presented in a comparative manner, thereby focusing on interesting clusters and retention times of nutritionally valuable tomato lines.

UR - http://www.scopus.com/inward/record.url?scp=85036478990&partnerID=8YFLogxK

U2 - 10.1142/9789812774118_0078

DO - 10.1142/9789812774118_0078

M3 - Conference Paper

SP - 549

EP - 556

BT - Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006

PB - World Scientific Publishing

ER -

Strickert M, Czauderna T, Peterek S, Matros A, Mock HP, Seiffert U. Full-length HPLC signal clustering and biomarker identification in tomato plants. In Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006. World Scientific Publishing. 2006. p. 549-556 https://doi.org/10.1142/9789812774118_0078