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Compendium Paper information and status A.
Chebira, Y. Barbotin, C. Jackson, T. Merryman, G. Srinivasa, R. F. Murphy and
J. Kovačević, “A multiresolution approach to automated
classification of protein subcellular location images,” BMC
Bioinformatics, vol. 8, no. 210, 2007. [pdf
| article at BMC
website] Abstract 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. Data 2D and 3D HeLa data sets
available from MurphyLab. Code The zipped archive contains
the readme file as well as the code to generate all the figures and tables in
the paper. This work is licensed under
the Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States
License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/us/. [download] Pseudo-code The zipped archive contains
the pseudo code for the algorithms in the paper. [download] Proofs NA Other material The zipped archive contains
Table 1 with variances included. [download] List of tested configurations Matlab
7.0.1 on Linux (Rocks) For more information or to
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