CLASSIFICATION OF MASS SPECTROMETRY DATA - Using Manifold and Supervised Distance Metric Learning

Qingzhong Liu, Andrew H. Sung, Bernardete M. Ribeiro, Mengyu Qiao

2009

Abstract

Mass spectrometry becomes the most widely used measurement in proteomics research. The quality of the feature set and applied learning classifier determine the reliability of the prediction of disease status. A well-known approach is to combine peak detection and support vector machine recursive feature elimination (SVMRFE). To compare the feature selection and to search for alternative learning classifier, in this paper, we employ a distance metric learning to classification of proteomics mass spectrometry (MS) data. Experimental results show that distance metric learning is promising for the classification of proteomics data; the results are comparable to the best results by applying SVM to the SVMRFE feature sets. Results also indicate that the good potential of manifold learning for feature reduction in MS data analysis.

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Paper Citation


in Harvard Style

Liu Q., H. Sung A., M. Ribeiro B. and Qiao M. (2009). CLASSIFICATION OF MASS SPECTROMETRY DATA - Using Manifold and Supervised Distance Metric Learning . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 396-401. DOI: 10.5220/0001556403960401


in Bibtex Style

@conference{biosignals09,
author={Qingzhong Liu and Andrew H. Sung and Bernardete M. Ribeiro and Mengyu Qiao},
title={CLASSIFICATION OF MASS SPECTROMETRY DATA - Using Manifold and Supervised Distance Metric Learning },
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={396-401},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001556403960401},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - CLASSIFICATION OF MASS SPECTROMETRY DATA - Using Manifold and Supervised Distance Metric Learning
SN - 978-989-8111-65-4
AU - Liu Q.
AU - H. Sung A.
AU - M. Ribeiro B.
AU - Qiao M.
PY - 2009
SP - 396
EP - 401
DO - 10.5220/0001556403960401