2019

Journal

Soft Computing 23(17): 7671-7697

The extraction of knowledge from large biological data is among the main challenges of bioinformatics. Several data mining techniques have been proposed to extract data; in this work, we focus on biclustering which has grown considerably in recent years. Biclustering aims to extract a set of genes with similar behavior under a condition set. In this paper, we propose an evolutionary biclustering algorithm and we analyze its performance by varying its genetic components. Hence, several versions of the evolutionary biclustering algorithm are introduced. Further, an experimental study is achieved on two real microarray datasets and the results are compared to other state-of-the-art biclustering algorithms. This thorough study allows to retain the best combination of operators among the various experienced choices.

@article{MaatoukABD19,
  author       = {Ons Ma{\^{a}}touk and
                  Wassim Ayadi and
                  Hend Bouziri and
                  B{\'{e}}atrice Duval},
  title        = {Evolutionary biclustering algorithms: an experimental study on microarray
                  data},
  journal      = {Soft Comput.},
  volume       = {23},
  number       = {17},
  pages        = {7671--7697},
  year         = {2019},
  doi          = {10.1007/S00500-018-3394-4}
}