  In this paper a novel biclustering algorithm based on artificial intelligence
(AI) is introduced. The method called EBIC aims to detect biologically
meaningful, order-preserving patterns in complex data. The proposed algorithm
is probably the first one capable of discovering with accuracy exceeding 50%
multiple complex patterns in real gene expression datasets. It is also one of
the very few biclustering methods designed for parallel environments with
multiple graphics processing units (GPUs). We demonstrate that EBIC outperforms
state-of-the-art biclustering methods, in terms of recovery and relevance, on
both synthetic and genetic datasets. EBIC also yields results over 12 times
faster than the most accurate reference algorithms. The proposed algorithm is
anticipated to be added to the repertoire of unsupervised machine learning
algorithms for the analysis of datasets, including those from large-scale
genomic studies.
