  Biclustering is a data mining technique which searches for local patterns in
numeric tabular data with main application in bioinformatics. This technique
has shown promise in multiple areas, including development of biomarkers for
cancer, disease subtype identification, or gene-drug interactions among others.
In this paper we introduce EBIC.JL - an implementation of one of the most
accurate biclustering algorithms in Julia, a modern highly parallelizable
programming language for data science. We show that the new version maintains
comparable accuracy to its predecessor EBIC while converging faster for the
majority of the problems. We hope that this open source software in a
high-level programming language will foster research in this promising field of
bioinformatics and expedite development of new biclustering methods for big
data.
