Robust Numerical Features for Description and Classification of Subcellular Location Patterns in Fluorescence Microscope Images: Special Issue on Signal Processing and Neural Networks for Bioinformatics

Authors: Murphy R.F.; Velliste M.; Porreca G.

Source: The Journal of VLSI Signal Processing, Volume 35, Number 3, 200311 , pp. 311-321(11)

Publisher: Springer

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Abstract:

The ongoing biotechnology revolution promises a complete understanding of the mechanisms by which cells and tissues carry out their functions. Central to that goal is the determination of the function of each protein that is present in a given cell type, and determining a protein's location within cells is critical to understanding its function. As large amounts of data become available from genome-wide determination of protein subcellular location, automated approaches to categorizing and comparing location patterns are urgently needed. Since subcellular location is most often determined using fluorescence microscopy, we have developed automated systems for interpreting the resulting images. We report here improved numeric features for describing such images that are fairly robust to image intensity binning and spatial resolution. We validate these features by using them to train neural networks that accurately recognize all major subcellular patterns with an accuracy higher than any previously reported. Having validated the features by using them for classification, we also demonstrate using them to create Subcellular Location Trees that group similar proteins and provide a systematic framework for describing subcellular location.

Keywords: protein localization; subcellular location features; fluorescence microscopy; pattern recognition; location proteomics

Document Type: Research article

Affiliations: 1: Departments of Biological Sciences and Biomedical Engineering, Carnegie Mellon University, 4400 Fifth Avenue, Pittsburgh, PA 15213, USA

Publication date: 2003-01-01