title: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.
abstract: 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
authors.affiliation: Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA.
authors.forename: M V
authors.initials: MV
authors.lastname: Boland
authors.forename: R F
authors.initials: RF
authors.lastname: Murphy
mesh_terms: 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
chemical_list: D011506:Proteins