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Biclustering of Expression Data with Evolutionary Computation
May 2006 (vol. 18 no. 5)
pp. 590-602
ASCII Text
x 
 
Federico Divina, Jes?s S. Aguilar-Ruiz, "Biclustering of Expression Data with Evolutionary Computation," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 590-602, May, 2006.
 
 
BibTex
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@article{ 10.1109/TKDE.2006.74,
author = {Federico Divina and Jes?s S. Aguilar-Ruiz},
title = {Biclustering of Expression Data with Evolutionary Computation},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {18},
number = {5},
issn = {1041-4347},
year = {2006},
pages = {590-602},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2006.74},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Biclustering of Expression Data with Evolutionary Computation
IS - 5
SN - 1041-4347
SP590
EP602
EPD - 590-602
A1 - Federico Divina,
A1 - Jes?s S. Aguilar-Ruiz,
PY - 2006
KW - Biclustering
KW - gene expression data
KW - evolutionary computation.
VL - 18
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Microarray techniques are leading to the development of sophisticated algorithms capable of extracting novel and useful knowledge from a biomedical point of view. In this work, we address the biclustering of gene expression data with evolutionary computation. Our approach is based on evolutionary algorithms, which have been proven to have excellent performance on complex problems, and searches for biclusters following a sequential covering strategy. The goal is to find biclusters of maximum size with mean squared residue lower than a given \delta. In addition, we pay special attention to the fact of looking for high-quality biclusters with large variation, i.e., with a relatively high row variance, and with a low level of overlapping among biclusters. The quality of biclusters found by our evolutionary approach is discussed and the results are compared to those reported by Cheng and Church, and Yang et al. In general, our approach, named SEBI, shows an excellent performance at finding patterns in gene expression data.

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Index Terms:
Biclustering, gene expression data, evolutionary computation.
Citation:
Federico Divina, Jes?s S. Aguilar-Ruiz, "Biclustering of Expression Data with Evolutionary Computation," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 5, pp. 590-602, May 2006, doi:10.1109/TKDE.2006.74
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