compendium

A. Chebira, Y. Barbotin, C. Jackson, T. E. Merryman, G. Srinivasa, R. F. Murphy and J. Kovačević. A multiresolution approach to automated classification of protein subcellular location images. BMC Bioinformatics, 8(210), 2007.

[ pdf | @ BMC website | bibtex]

Background: Fluorescence microscopy is widely used to determine the subcellular location of proteins. Efforts to determine location on a proteome-wide basis create a need for automated methods to analyze the resulting images. Over the past ten years, the feasibility of using machine learning methods to recognize all major subcellular location patterns has been convincingly demonstrated, using diverse feature sets and classifiers. On a well-studied data set of 2D HeLa single-cell images, the best performance to date, 91.5%, was obtained by including a set of multiresolution features. This demonstrates the value of multiresolution approaches to this important problem.

Results: We report here a novel approach for the classification of subcellular location patterns by classifying in multiresolution subspaces. Our system is able to work with any feature set and any classifier. It consists of multiresolution (MR) decomposition, followed by feature computation and classification in each MR subspace, yielding local decisions that are then combined into a global decision. With 26 texture features alone and a neural network classifier, we obtained an increase in accuracy on the 2D HeLa data set to 95.3%.

Conclusions: We demonstrate that the space-frequency localized information in the multiresolution subspaces adds significantly to the discriminative power of the system. Moreover, we show that a vastly reduced set of features is sufficient, consisting of our novel modified Haralick texture features. Our proposed system is general, allowing for any combinations of sets of features and any combination of classifiers.

2D and 3D HeLa data sets available from MurphyLab

matlab

The zipped archive contains the readme file as well as the code to generate all the figures and tables in the paper.

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This work is licensed under a Creative Commons GNU General Public License. To view a copy of this license, visit http://creativecommons.org/licenses/GPL/2.0. If you use this code or any part thereof in your research or publication, please also include a reference to this paper. Thank you!

The zipped archive contains the pseudo code for the algorithms in the paper.

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The zipped archive contains Table 1 with variances included.

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Matlab 7.0.1 on Linux (Rocks)

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