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Bioinformatics. 2001 Dec;17(12):1213-23.

A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells.

Author information

  • 1Center for Light Microscope Imaging and Biotechnology, Biomedical and Health Engineering Program, Carnegie Mellon University, 4400 Fifth Ave., Pittsburgh, PA 15213, USA.

Abstract

MOTIVATION:

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.

AVAILABILITY:

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.

CONTACT:

murphy@cmu.edu

PMID:
11751230
[PubMed - indexed for MEDLINE]
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