Weighted Elastic Net Model for Mass Spectrometry Imaging Processing

D. Hong; F. Zhang

Mathematical Modelling of Natural Phenomena (2010)

  • Volume: 5, Issue: 3, page 115-133
  • ISSN: 0973-5348

Abstract

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In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promising new technique for protein analysis from intact biological tissues. Though it has shown great potential and is very promising for rapid mapping of protein localization and the detection of sizeable differences in protein expression, challenges remain in data processing due to the difficulty of high dimensionality and the fact that the number of input variables in prediction model is significantly larger than the number of observations. To obtain a complete overview of IMS data and find trace features based on both spectral and spatial patterns, one faces a global optimization problem. In this paper, we propose a weighted elastic net (WEN) model based on IMS data processing needs of using both the spectral and spatial information for biomarker selection and classification. Properties including variable selection accuracy of the WEN model are discussed. Experimental IMS data analysis results show that such a model not only reduces the number of side features but also helps new biomarkers discovery.

How to cite

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Hong, D., and Zhang, F.. "Weighted Elastic Net Model for Mass Spectrometry Imaging Processing." Mathematical Modelling of Natural Phenomena 5.3 (2010): 115-133. <http://eudml.org/doc/197686>.

@article{Hong2010,
abstract = {In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promising new technique for protein analysis from intact biological tissues. Though it has shown great potential and is very promising for rapid mapping of protein localization and the detection of sizeable differences in protein expression, challenges remain in data processing due to the difficulty of high dimensionality and the fact that the number of input variables in prediction model is significantly larger than the number of observations. To obtain a complete overview of IMS data and find trace features based on both spectral and spatial patterns, one faces a global optimization problem. In this paper, we propose a weighted elastic net (WEN) model based on IMS data processing needs of using both the spectral and spatial information for biomarker selection and classification. Properties including variable selection accuracy of the WEN model are discussed. Experimental IMS data analysis results show that such a model not only reduces the number of side features but also helps new biomarkers discovery.},
author = {Hong, D., Zhang, F.},
journal = {Mathematical Modelling of Natural Phenomena},
keywords = {biomarker discovery; weighted elastic-net; mass spectrometry imaging; penalized regression; variable selection},
language = {eng},
month = {4},
number = {3},
pages = {115-133},
publisher = {EDP Sciences},
title = {Weighted Elastic Net Model for Mass Spectrometry Imaging Processing},
url = {http://eudml.org/doc/197686},
volume = {5},
year = {2010},
}

TY - JOUR
AU - Hong, D.
AU - Zhang, F.
TI - Weighted Elastic Net Model for Mass Spectrometry Imaging Processing
JO - Mathematical Modelling of Natural Phenomena
DA - 2010/4//
PB - EDP Sciences
VL - 5
IS - 3
SP - 115
EP - 133
AB - In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promising new technique for protein analysis from intact biological tissues. Though it has shown great potential and is very promising for rapid mapping of protein localization and the detection of sizeable differences in protein expression, challenges remain in data processing due to the difficulty of high dimensionality and the fact that the number of input variables in prediction model is significantly larger than the number of observations. To obtain a complete overview of IMS data and find trace features based on both spectral and spatial patterns, one faces a global optimization problem. In this paper, we propose a weighted elastic net (WEN) model based on IMS data processing needs of using both the spectral and spatial information for biomarker selection and classification. Properties including variable selection accuracy of the WEN model are discussed. Experimental IMS data analysis results show that such a model not only reduces the number of side features but also helps new biomarkers discovery.
LA - eng
KW - biomarker discovery; weighted elastic-net; mass spectrometry imaging; penalized regression; variable selection
UR - http://eudml.org/doc/197686
ER -

References

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