  Motivation: In this paper we present the latest release of EBIC, a
next-generation biclustering algorithm for mining genetic data. The major
contribution of this paper is adding support for big data, making it possible
to efficiently run large genomic data mining analyses. Additional enhancements
include integration with R and Bioconductor and an option to remove influence
of missing value on the final result.
  Results: EBIC was applied to datasets of different sizes, including a large
DNA methylation dataset with 436,444 rows. For the largest dataset we observed
over 6.6 fold speedup in computation time on a cluster of 8 GPUs compared to
running the method on a single GPU. This proves high scalability of the
algorithm.
  Availability: The latest version of EBIC could be downloaded from
http://github.com/EpistasisLab/ebic . Installation and usage instructions are
also available online.
