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Bioinformatics Advance Access originally published online on August 9, 2005
Bioinformatics 2005 21(19):3778-3786; 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{at}oxfordjournals.org
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A hybrid machine-learning approach for segmentation of protein localization data

Peter M. Kasson 1,2, Johannes B. Huppa 5,6, Mark M. Davis 5,6 and Axel T. Brunger 3,4,5,*

1Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University Stanford, CA 94305, USA
2Medical Scientist Training Program, Stanford Synchrotron Radiation Laboratory, Stanford University Stanford, CA 94305, USA
3Department of Molecular and Cellular Physiology, Stanford Synchrotron Radiation Laboratory, Stanford University Stanford, CA 94305, USA
4Department of Neurology and Neurological Sciences, Stanford Synchrotron Radiation Laboratory, Stanford University Stanford, CA 94305, USA
5Howard Hughes Medical Institute Stanford, CA 94305, USA
6Department of Microbiology and Immunology, Stanford University School of Medicine Stanford, CA 94305, USA

*To whom correspondence should be addressed.

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 a support vector machine.

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 cyielded 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

Contact: brunger{at}stanford.edu

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

Received on March 2, 2005; revised on May 30, 2005; accepted on August 4, 2005

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