A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.
BACKGROUND
Assessment of protein subcellular location is crucial to proteomics efforts since localization information provides a context for a protein's sequence, structure, and function. The work described below is the first to address the subcellular localization of proteins in a quantitative, comprehensive manner.


RESULTS
Images for ten different subcellular patterns (including all major organelles) were collected using fluorescence microscopy. The patterns were described using a variety of numeric features, including Zernike moments, Haralick texture features, and a set of new features developed specifically for this purpose. To test the usefulness of these features, they were used to train a neural network classifier. The classifier was able to correctly recognize an average of 83% of previously unseen cells showing one of the ten patterns. The same classifier was then used to recognize previously unseen sets of homogeneously prepared cells with 98% accuracy.


BACKGROUND
Algorithms were implemented using the commercial products Matlab, S-Plus, and SAS, as well as some functions written in C. The scripts and source code generated for this work are available at http://murphylab.web.cmu.edu/software.


BACKGROUND
murphy@cmu.edu
Boland
M V
MV
Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA.
Murphy
R F
RF
D000465:Algorithms; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008856:Microscopy, Fluorescence / Q000379:methods; D016571:Neural Networks, Computer; D011506:Proteins / Q000032:analysis*; D013347:Subcellular Fractions / Q000737:chemistry
D011506:Proteins