Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
532345 | Pattern Recognition | 2012 | 12 Pages |
Several biclustering algorithms have been proposed in different fields of microarray data analysis. We present a new approach that improves their performance in using the ensemble methods. An ensemble biclustering is considered and formalized by a problem of binary triclustering. We propose a simple and efficient algorithm to solve it. To illustrate the interest of our ensemble approach, numerical experiments are performed on both artificial and real datasets with two biclustering algorithms commonly used in bioinformatics.
► We propose a new method of biclustering based on ensemble methods. ► We analyze the impact of the different parameters on the biclustering performance. ► Our methods improve the performance of biclustering. ► We show that our method identifies several biclusters biologically relevant in microarray datasets.