Title :
Biclustering of expression data with evolutionary computation
Author :
Divina, Federico ; Aguilar-Ruiz, JesÙs S.
Author_Institution :
Tilburg Univ., Netherlands
fDate :
5/1/2006 12:00:00 AM
Abstract :
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 δ. 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.
Keywords :
biology computing; data mining; evolutionary computation; genetics; biclustering; evolutionary algorithm; evolutionary computation; gene expression data; knowledge extraction; mean squared residue; microarray technique; sequential covering strategy; Bioinformatics; Data mining; Databases; Diseases; Equations; Evolutionary computation; Gene expression; Genomics; Biclustering; evolutionary computation.; gene expression data;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2006.74