2012

Journal

Knowl. Inf. Syst. 30(2): 341-358

In the context of microarray data analysis, biclustering allows the simultaneous identification of a maximum group of genes that show highly correlated expression patterns through a maximum group of experimental conditions (samples). This paper introduces a heuristic algorithm called BicFinder (The BicFinder software is available at: http://www.info.univ-angers.fr/pub/hao/BicFinder.html) for extracting biclusters from microarray data. BicFinder relies on a new evaluation function called Average Correspondence Similarity Index (ACSI) to assess the coherence of a given bicluster and utilizes a directed acyclic graph to construct its biclusters. The performance of BicFinder is evaluated on synthetic and three DNA microarray datasets. We test the biological significance using a gene annotation web-tool to show that our proposed algorithm is able to produce biologically relevant biclusters. Experimental results show that BicFinder is able to identify coherent and overlapping biclusters.

@article{AyadiEH12K,
  author       = {Wassim Ayadi and
                  Mourad Elloumi and
                  Jin{-}Kao Hao},
  title        = {BicFinder: a biclustering algorithm for microarray data analysis},
  journal      = {Knowl. Inf. Syst.},
  volume       = {30},
  number       = {2},
  pages        = {341--358},
  year         = {2012},
  url          = {https://doi.org/10.1007/s10115-011-0383-7},
  doi          = {10.1007/S10115-011-0383-7}
}