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Bioinformatics Advance Access published online on August 9, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti615
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© The Author (2005). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org
Received March 2, 2005
Revised May 30, 2005
Accepted August 4, 2005

Article

A hybrid machine-learning approach for segmentation of protein localization data

Peter M. Kasson 1, Johannes B. Huppa 2, Mark M. Davis 2, and Axel T. Brunger 3*

1 Biophysics Program, Stanford University, Stanford, California 94305 USA; Medical Scientist Training Program, Stanford University, Stanford, California 94305 USA
2 Howard Hughes Medical Institute, Stanford, California 94305 USA; Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, California 94305 USA
3 Howard Hughes Medical Institute, Stanford, California 94305 USA; Department of Molecular and Cellular Physiology, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, California 94305 USA; Department of Neurology and Neurological Sciences, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, California 94305 USA

* To whom correspondence should be addressed.
Axel T. Brunger, E-mail: brunger{at}stanford.edu


  Abstract

Motivation: Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for an SVM.

Results: We have employed this classifier to analyze signaling protein localization in T-cell activation. Our classifier displayed increased performance over previously available techniques, exhibiting both flexibility and adaptability: training on heterogeneous data yielded a general classifier with good overall performance; training on more specific data yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.

Availability: http://atb.slac.stanford.edu/~kasson/membraneclassifier.html.

Supplementary Information: http://atb.slac.stanford.edu/~kasson/classifier_suppl.pdf.


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