A hybrid machine-learning approach for segmentation of protein localization data.
BACKGROUND
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 yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.
Kasson
Peter M
PM
Biophysics Program, Stanford Synchrotron Radiation Laboratory, Stanford University, Stanford, CA 94305, USA.
Huppa
Johannes B
JB
Davis
Mark M
MM
Brunger
Axel T
AT
D000818:Animals; D001185:Artificial Intelligence; D002460:Cell Line; D020869:Gene Expression Profiling / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008213:Lymphocyte Activation / Q000502:physiology*; D051379:Mice; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated; D011948:Receptors, Antigen, T-Cell / Q000378:metabolism*; D013050:Spectrometry, Fluorescence / Q000379:methods*; D018511:Systems Integration; D013601:T-Lymphocytes / Q000166:cytology / Q000378:metabolism*; D014018:Tissue Distribution
D011948:Receptors, Antigen, T-Cell